The majority of NLG evaluation relies on automatic metrics, such as BLEU. In this paper, we investigate a wide range of these metrics, including state-of-the-art wordbased and novel grammar-based ones, and demonstrate that they only weakly reflect human judgements of system outputs as generated by data-driven, end-toend NLG. A detailed error analysis shows that automatic metrics are particularly bad in distinguishing outputs of medium and good quality, which can be partially attributed to the fact that human judgements and metrics are given on different scales. We also show that metric performance is dataand system-specific. We then suggest an alternative metric, called RAINBOW, combining the individual strengths of different automatic scores. This new metric achieves up to ρ = 0.81 correlation with human judgements at the sentence-level (compared to a maximum of ρ = 0.33 for existing metrics) and achieves stable results across systems and datasets.
1 Introduction
Automatic evaluation measures, such as BLEU (Papineni et al., 2002), are used with increasing frequency to evaluate Natural Language Generation (NLG) systems: Up to 60% of NLG research published between 2012–2015 relies on automatic metrics (Gkatzia and Mahamood, 2015). Automatic evaluation is popular because it is cheaper and faster to run than human evaluation, and is needed for automatic benchmarking and tuning of algorithms. The use of such metrics is, however, only sensible if they are known to be sufficiently correlated with human preferences. This is rarely
the case, as shown by various studies in NLG (Reiter and Belz, 2009; Belz and Reiter, 2006; Stent et al., 2005), as well as in related fields, such as dialogue systems (Liu et al., 2016), machine translation (MT), e.g. (Callison-Burch et al., 2006), and image captioning, e.g. (Elliott and Keller, 2014; Kilickaya et al., 2017). This paper follows on from this work and presents another evaluation study into automatic metrics with the aim to firmly establish the need for new metrics. We also suggest an alternative metric, which we call RAINBOW to reflect the diverse set of features it is based upon. In contrast to previous work, we are the first to: • Target end-to-end data-driven NLG, where we compare 3 different approaches. In contrast to NLG methods evaluated in previous work, our systems can produce ungrammatical output by (1) generating word-by-word, and (2) learning from noisy data. • Compare a large number of 21 automated metrics, including grammar-based ones. • Report results on two different domains and three different datasets, which allows us to draw more general conclusions. • Suggest an alternative automatic metric, which shows high correlation with human judgements. • Conduct a detailed error analysis, which suggests that, while metrics can be reasonable indicators at the system-level, they are not reliable at the sentence-level. •Make all associated code and data publicly available, including detailed analysis results.
2 End-to-End NLG Systems
In this paper, we focus on recent end-to-end, datadriven NLG methods, which jointly learn sentence planning and surface realisation from non-aligned data (Dušek and Jurčı́ček, 2015; Wen et al., 2015; Mei et al., 2016; Wen et al., 2016; Sharma et al.,
2
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
2016; Dušek and Jurčı́ček, 2016; Lampouras and Vlachos, 2016). These approaches do not require costly semantic alignment between Meaning Representations (MR) and human references (also referred to as “ground truth” or “targets”), but are based on parallel datasets, which can be collected in sufficient quality and quantity using effective crowd-sourcing techniques, e.g. (Novikova et al., 2016), and as such, enable rapid development of NLG components in new domains. In particular, we compare the performance of the following systems: • RNNLG:1 The system by Wen et al. (2015) uses a Long Short-term Memory (LSTM) network to jointly address sentence planning and surface realisation. It augments each LSTM cell with a gate that conditions it on the input MR, which allows it to keep track of MR contents generated so far. • TGEN:2 The system by Dušek and Jurčı́ček (2015) learns to incrementally generate deepsyntax dependency trees of candidate sentence plans (i.e., which MR elements to mention and the overall sentence structure). Surface realisation is performed using a separate, domain-independent rule-based module. • LOLS:3 The system by Lampouras and Vlachos (2016) learns sentence planning and surface realisation using Locally Optimal Learning to Search (LOLS), an imitation learning framework which learns using BLEU and ROUGE as non-decomposable loss functions.
sys/ data SFHOTEL SFREST BAGEL RNNLG X X TGEN X LOLS X X X
Table 1: Systems and datasets used in this study.
3 Datasets
We consider the following datasets collected via crowd-sourcing, which target utterance generation for spoken dialogue systems. Table 1 shows which NLG system was trained on which dataset. Each instance consists of one MR and one or more natural language references as produced by humans, such as the following example, taken from the BAGEL dataset. Note that we use lexicalised ver-
1https://github.com/shawnwun/RNNLG 2https://github.com/UFAL-DSG/tgen 3https://github.com/glampouras/JLOLS_
NLG
sions of SFHOTEL and SFREST and a partially lexicalised version of BAGEL, where proper names and place names are replaced by placeholders (X), in correspondence with the outputs generated by the systems, as provided by the system authors. MR: inform(name=X, area=X, pricerange=moderate, type=restaurant) Reference: “X is a moderately priced restaurant in X.”
• SFHOTEL & SFREST (Wen et al., 2015) provide information about hotels and restaurants in San Francisco. There are 8 system dialogue act types, such as inform, confirm, goodbye etc. Each domain contains 12 attributes, where some are common to both domains, such as name, type, pricerange, address, area, etc., and the others are domain-specific, e.g. food and kids-allowed for restaurants; hasinternet and dogs-allowed for hotels. For each domain, around 5K human references were collected with 2.3K unique human utterances for SFHOTEL and 1.6K for SFREST. The number of unique system outputs produced is 1181 for SFREST and 875 for SFHOTEL. • BAGEL (Mairesse et al., 2010) provides information about restaurants in Cambridge. The dataset contains 202 aligned pairs of MRs and 2 corresponding references each. The domain is a subset of SFREST, including only the inform act and 8 attributes.
4 Metrics
4.1 Word-based metrics (WBMs)
NLG evaluation has borrowed a number of automatic metrics from related fields, such as MT, summarisation, or image captioning, which compare output texts generated by systems to groundtruth references produced by humans. We refer to this group as word-based metrics. In general, the higher these scores are, the better or more similar to the human references the output is.4 The following order reflects the degree these metrics move from simple n-gram overlap to also considering term frequency (TF-IDF) weighting and semantically similar words. •Word-overlap metrics: We consider frequently used metrics, including TER (Snover et al., 2006), BLEU (Papineni et al., 2002), ROUGE (Lin, 2004), NIST (Doddington, 2002), LEPOR (Han et al., 2012), CIDEr (Vedantam et al., 2015), and METEOR (Lavie and Agarwal, 2007).
4Except for TER whose scale is reversed.
3
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
• Semantic Similarity (SIM): We calculate the Semantic Text Similarity measure provided by Han et al. (2013). This measure is based on distributional similarity and Latent Semantic Analysis (LSA) and is further complemented with semantic relations extracted from WordNet.
4.2 Grammar-based metrics (GBMs)
Grammar-based measures have been explored in related fields, such as MT (Giménez and Màrquez, 2008) or grammatical error correction (Napoles et al., 2016), and, in contrast to WBMs, do not rely on ground-truth references. To our knowledge, we are the first to consider GBMs for sentence-level NLG evaluation.
• Readability quantifies the difficulty with which a reader understands a text, as used for e.g. evaluating summarisation (Kan et al., 2001) or text simplification (Francois and Bernhard, 2014). We measure readability by the Flesch Reading Ease score (RE) (Flesch, 1979), which calculates a ratio between the number of characters per sentence, the number of words per sentence, and the number of syllables per word. Higher readability score indicates a less complex utterance that is easier to read. We also consider related measures, such as characters per utterance (len) and per word (cpw), words per sentence (wps), syllables per sentence (sps) and per word (spw), as well as polysyllabic words per utterance (pol) and per word (ppw). The higher these scores, the more complex the utterance.
• Grammaticality: In contrast to previous NLG methods, our corpus-based systems can produce ungrammatical output by (1) generating word-byword, and (2) learning from noisy data. As a first approximation of grammaticality, we measure the parsing score (prs) as returned by the Stanford parser, as well as the number of misspellings (msp). The lower these scores are, the more grammatically correct an utterance is. Note that the Stanford parser score is not designed to measure grammaticality, however, it will generally prefer a grammatical parse to a non-grammatical one.5 In future work, we aim to use specifically designed grammar-scoring functions, e.g. (Napoles et al., 2016), once they become publicly available.
5http://nlp.stanford.edu/software/ parser-faq.shtml
5 Human Data Collection
To collect human rankings, we presented the MR together with 2 utterances generated by different systems side-by-side to crowdworkers, which were asked to score each utterance on a 6-point Likert scale for: • Informativeness: Does the utterance provide all the useful information from the meaning representation? • Naturalness: Could the utterance have been produced by a native speaker? • Quality: How do you judge the overall quality of the utterance in terms of its grammatical correctness and fluency?
Each system output was scored by 3 different crowdworkers. To reduce participants’ bias, the order of appearance of utterances produced by each system was randomised and crowdworkers were restricted to evaluate a maximum of 20 utterances. The crowdworkers were selected from English-speaking countries only, based on their IP-addresses, and asked to confirm that English was their native language.
To assess the reliability of ratings, we calculated the intra-class correlation coefficient (ICC), which measures inter-observer reliability on ordinal data for more than two raters (Landis and Koch, 1977). The overall ICC across all three datasets is 0.45 (p <0.001), which corresponds to a moderate agreement. In general, we find consistent differences in inter-annotator agreement per system and dataset, with lower agreements in LOLS than in RNNLG and TGEN. Agreement is highest for the SFHOTEL dataset, followed by SFREST and BAGEL (for details see Appendix A, Table 7).
6 System Evaluation
Table 2 summarises the individual systems’ performance in terms of automatic and human scores.6 All WBMs produce the same (significant) results, whereas GBMs show the same trend, but with different levels of statistical significance, with only len, wps and sps producing reliable significant results. System performance is datasetspecific: For WBMs, the LOLS system consistently produces better results on BAGEL compared to TGEN, while for SFREST and SFHOTEL, LOLS is outperformed by RNNLG with WBMs. We observe that human informativeness ratings follow
6Detailed results are submitted as supplementary material. See Appendix A, Table 8.
4
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
BAGEL SFHOTEL SFREST
metric TGEN LOLS RNNLG LOLS RNNLG LOLS
WBMs More overlap More overlap* More overlap* SIM More similar More similar* More similar RE More complex(*) More complex(*) More complex(*) GBMs Better grammar(*) Better grammar(*) Better grammar inform 4.77(Sd=1.09) 4.91(Sd=1.23) 5.47*(Sd=0.81) 5.27(Sd=1.02) 5.29*(Sd=0.94) 5.16(Sd=1.07) natural 4.76(Sd=1.26) 4.67(Sd=1.25) 4.99*(Sd=1.13) 4.62(Sd=1.28) 4.86(Sd=1.13) 4.74(Sd=1.23) quality 4.77(Sd=1.19) 4.54(Sd=1.28) 4.54(Sd=1.18) 4.53(Sd=1.26) 4.51(Sd=1.14) 4.58(Sd=1.33)
Table 2: System performance per dataset (‘*’ denotes p<0.05).
the same pattern as WBMs, while the average similarity score (sim) seems to be related to human quality ratings.
Looking at GBMs, we observe that they seem to be related to naturalness and quality ratings. Less complex utterances, as measured by readability (RE) and word length (cpw), have higher naturalness ratings. More complex utterances, as measured in terms of their length (len), number of words (wps), syllables (sps, spw) and polysyllables (pol, ppw), have lower quality evaluation. Utterances measured as more grammatical are on average evaluated higher in terms of naturalness.
While these initial results may suggest a relation between automatic metrics and human ratings, average scores can be misleading, as they only provide a system-level overview but do not measure the strength of association on sentence-level. This leads us to inspect the correlation of human and automatic metrics for each MR-system output pair.
7 Relation of Human and Automatic Metrics
7.1 Human Correlation Analysis
We calculate the correlation between automatic metrics and human ratings using the Spearman coefficient (ρ). We split the data per dataset and system in order to make valid pairwise comparisons. To handle outliers within human ratings, we use the median score of the three human raters. Following Kilickaya et al. (2017), we use the Williams’ test (Williams, 1959) to determine significant differences between correlations. Table 3 summarises the correlation results between automatic metrics and human ratings, listing the best (i.e., highest absolute ρ) results for each type of metric (see Appendix A, Table 9 for details). Our results suggest that: • In sum, no metric produces an even moderate correlation with human ratings, independently of dataset, system, or aspect of human rating. This
contrasts with our initially promising results on the system-level (see Section 6) and will be further discussed in Section 8. Note that similar inconsistencies between document- and sentencelevel evaluation results are observed in MT (Specia et al., 2010). • Similar to our previous results in Section 6, we find that WBMs show better correlations to human ratings of informativeness whereas GBMs show better correlations to quality and naturalness. • Human ratings for informativeness, naturalness and quality are highly correlated with each other, with the highest correlation between the latter two (ρ = 0.81) reflecting that they both target surface realisation. • All WBMs produce similar results (see Figure 1 and 2): They are strongly correlated with each other, and most of them produce correlations with human ratings which are not significantly different from each other. GBMs, on the other hand, show greater diversity. • Correlation results are system- and datasetspecific (also see Appendix A, Tables 10–11). We observe the highest correlation for TGEN on BAGEL (Figures 1 and 2) and LOLS on SFREST, whereas RNNLG often shows low correlation between metrics and human ratings. This lets us conclude that WBMs and GBMs are sensitive to different systems and datasets. • The highest positive correlation is observed between the number of words (wps) and informativeness for the TGEN system on BAGEL (ρ = 0.33, p < 0.01, see Figure 1). However, the wps metric (amongst most others) is not robust across systems and datasets: Its correlation on other datasets is very weak, (ρ ≤ .18) and its correlation with informativeness ratings of LOLS’s output is insignificant. • As a sanity check, we also measure a random score [0.0, 1.0] which proves to have a close-tozero correlation with human ratings (highest ρ = 0.09).
5
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
BAGEL SFHOTEL SFREST
TGEN LOLS RNNLG LOLS RNNLG LOLS
Best WBM inform 0.30* (B1) 0.20* (RG) 0.09 (B1) 0.14* (LEP) 0.13* (SIM) 0.28* (LEP) natural -0.19* (TER) -0.19* (TER) 0.10* (MET) -0.20* (TER) 0.17* (RG) 0.19* (MET) quality -0.16* (TER) 0.16* (MET) 0.10* (MET) -0.12* (TER) 0.09* (MET) 0.18* (LEP) Best GBM inform 0.33* (WPS) 0.16* (PPW) -0.09 (PPW) 0.13* (CPW) 0.11* (LEN) 0.21* (LEN) natural -0.25* (LEN) -0.28* (WPS) -0.17* (LEN) -0.18* (SPS) -0.19* (WPS) -0.21* (SPS) quality -0.19* (CPW) -0.31* (PRS) -0.16* (PPW) -0.17* (SPW) -0.11* (PRS) -0.16* (SPS)
Table 3: Spearman correlation between metrics and human ratings, with ‘*’ denoting p < 0.05. Best metrics show the highest absolute value of ρ.
Figure 1: Spearman correlation results for TGEN on BAGEL. Bordered area shows correlations between human ratings and automatic metrics, the rest shows correlations among the metrics.
Figure 2: Williams test results: X represents a non-significant difference between correlations (p < 0.05; top: WBMs, bottom: GBMs).
7.2 Accuracy of Relative Rankings
We now evaluate a more coarse measure, namely the metrics’ ability to predict relative human ratings. That is, we compute the score of each metric for two system output sentences corresponding to the same MR. The prediction of a metric is correct if it orders the sentences in the same way as median human ratings (note that ties are allowed). Following from previous work (Vedantam et al.,
2015; Kilickaya et al., 2017), we mainly concentrate on WBMs. Results summarised in Table 4 show that most metrics’ performance is not significantly different from that of a random score (Wilcoxon signed rank test), see details in Appendix A, Table 12. While the random score fluctuates between 25.4–44.5% prediction accuracy, the metrics achieve an accuracy of between 30.6– 49.8%. Again, the performance of the metrics is dataset-specific: Metrics perform best on BAGEL data; for SFHOTEL, metrics show mixed performance; while for SFREST, metrics perform worst. Discussion: Our data differs from the one used in previous work (Vedantam et al., 2015; Kilickaya et al., 2017), which uses explicit relative rankings (“Which output do you prefer?”), whereas we compare two Likert-scale ratings. As such, we have 3 possible outcomes (allowing ties). This way, we can account for equally valid system outputs, which is one of the main drawbacks of forced-choice approaches (Hodosh and Hockenmaier, 2016). In relation to previous work, our results are reasonable: Kilickaya et al. (2017) report results between 60-74% accuracy for binary
6
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
588
589
590
591
592
593
594
595
596
597
598
599
informat. naturalness quality
BAGEL raw data
TER, BLEU1-4, ROUGE, NIST, LEPOR, CIDEr, METEOR, SIM TER, BLEU1-4, ROUGE, NIST, LEPOR, CIDEr, METEOR, SIM TER, BLEU1-4, ROUGE, NIST, LEPOR, CIDEr, METEOR, SIM
SFHOTEL raw data
TER, BLEU1-4, ROUGE, LEPOR, CIDEr, METEOR, SIM METEOR N/A
SFREST raw data
SIM LEPOR N/A
quant. data TER, BLEU1-4, ROUGE, NIST, LEPOR, CIDEr, METEOR
SIM
N/A N/A
Table 4: Metrics predicting relative human rating with significantly higher than random accuracy.
classification on machine-machine data, which is comparable to our results for 3-way classification.
Also, we observe a mismatch between the ordinal human ratings and the continuous metrics. For example, humans might rate system A and system B both as a 6, whereas BLEU, for example, might assign 0.98 and 1.0 respectively, meaning that BLEU will declare system B as the winner. In order to account for this mismatch, we quantise our metric data to the same scale as the median scores from our human ratings.7 Applied to SFREST, where we previously got our worst results, we can see an improvement for predicting informativeness, where all metrics now perform significantly better than the random baseline, see Table 4. In future, we will investigate related discriminative approaches, e.g. (Hodosh and Hockenmaier, 2016; Kannan and Vinyals, 2017), where the task is simplified to distinguishing correct from incorrect output.
8 Error Analysis
In this section, we attempt to uncover why automatic metrics perform so poorly.
8.1 Scales
We first explore the hypothesis that metrics are good in distinguishing extreme cases, i.e. system outputs which are rated as clearly good or bad by the human judges, but do not perform well for utterances rated in the middle of the Likert scale, as suggested by Kilickaya et al. (2017). We ‘bin’ our data into three groups: bad, which comprises low
7Note that this mismatch can also be accounted for by continuous rating scales, as suggested by Belz and Kow (2011).
ratings (≤2); good, comprising high ratings (≥5); and finally a group comprising average ratings. We find that utterances with low human ratings of informativeness and naturalness correlate significantly better (p < 0.05) with automatic metrics than those with average and good human ratings. For example, as shown in Figure 3, the correlation between WBMs and human ratings for utterances with low informativeness scores ranges between 0.3 ≤ ρ ≤ 0.5 (moderate correlation), while the highest correlation for utterances of average and high informativeness barely reaches ρ ≤ 0.2 (very weak correlation). The same pattern can be observed for correlations with quality and naturalness ratings (see Appendix A, Table 13).
This discrepancy in correlation results between low and other user ratings, together with the fact that the majority of system outputs are rated “good” for informativeness (79%), naturalness (64%) and quality (58%), whereas low ratings do not exceed 7% in total, could explain why the overall correlations are low (Section 7) despite the observed trends in relationship between average system-level performance scores (Section 6). It also explains why the RNNLG system, which contains very few instances of low user ratings, shows poor correlation between human ratings and automatic metrics.
8.2 Impact of Target Data
Type of Data: In Section 7.1, we observed that datasets have a significant impact on how well automatic metrics reflect human ratings. A closer inspection shows that BAGEL data differs significantly from SFREST and SFHOTEL, both in terms
7
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
of grammatical and MR properties. BAGEL has significantly shorter references both in terms of number of characters and words compared to the other two datasets. Although being shorter, the words in BAGEL references are significantly more often polysyllabic. Furthermore, BAGEL only consists of utterances generated from inform MRs, while SFREST and SFHOTEL also have less complex MR types, such as confirm, goodbye, etc. Utterances produced from inform MRs are significantly longer and have a significantly higher correlation with human ratings of informativeness and naturalness, than non-inform type of utterances (see Appendix A, Table 14). In other words, BAGEL is the most complex dataset to generate from, but also the one where metrics perform most reliably (note that the correlation is still only weak). One possible explanation of this discrepancy is that BAGEL only contains 2 human references per MR, whereas SFHOTEL and SFREST both contain 5.35 references per MR on average. Having more references means that WBMs naturally will return higher scores (‘anything goes’). This problem could possibly be solved by weighting multiple references according to their quality, as suggested by (Galley et al., 2015), or following a referenceless approach (Specia et al., 2010). Quality of Data: Our corpora contain crowdsourced human references that have grammatical errors, e.g. “Fifth Floor does not allow childs” (SFREST reference), or tautology issues, e.g. “Do you want a hotel in the pricey price range?” (SFHOTEL reference). Corpus-based methods may pick up these errors, and word-based metrics will rate these system utterances as correct, whereas we can expect human judges to be sensitive to ungrammatical utterances. Note that the parsing score (while being a crude approximation of grammaticality) achieves one of our highest correlation results against human ratings, with |ρ| = .31. Grammatical errors raise questions about the quality of the training data, especially when being crowd-sourced. For example, Belz and Reiter (2006) find that human experts assign low rankings to their original corpus text. Again, weighting (Galley et al., 2015) or reference-less approaches (Specia et al., 2010) might remedy this issue.
9 Combined Model
In this section, we present our new metric, which we call RAINBOW to reflect that it combines mul-
tiple other metrics. Recent results in MT (Yu et al., 2015; Bojar et al., 2016) show that combining multiple metrics can achieve the best overall correlation with human ratings at the sentencelevel. This motivates us to combine the respective strengths of WBMs and GBMs into a single model using ensemble learning (Random Forest, RF) (Breiman, 2001).
Setup: We use quantised metrics scores, as described in Section 7.2, and median human ratings, with a 70/30% split for training and testing and 10-fold cross-validation on the training data to tune the optimal number of predictors selected for growing trees. 100 trees were grown with 2 variables randomly sampled as candidates at each split. We investigate four different models: 1) all WBMs and GBMs metrics combined as predictors (RAINBOW WBM+GBM), 2) all WBMs combined (RAINBOW WBM), 3) all GBMs combined (RAINBOW GBM), and 4) only top-5 predictors combined (RAINBOW Top5). Results: The results in Table 5 show that the combined metrics are both able to increase the correlation with human ratings and behave in a robust way across different datasets and systems. The highest correlation is achieved when all the automatic metrics are combined (0.71 ≤ ρ ≤ 0.81). The models which only combine WBMs or only combine GBMs correlate significantly worse with human ratings (p < 0.001, Williams test). This supports our hypothesis that a combination of WBMs and GBMs can overcome the weaknesses of both.
Computing a full set of automatic metrics (21 in our case) as the input for the ensemble model is time consuming, which is especially problematic if the metric is used as a loss function, as e.g. in (Lampouras and Vlachos, 2016; Li et al., 2017). To overcome this limitation, we select the top 5 features (according to the RF model), using a recursive feature elimination. The top 5 features for predicting informativeness are len, SIM, METEOR, cpw and ROUGE; for naturalness – wps, METEOR, cpw, sps and TER; and for quality – METEOR, ROUGE, sps, BLEU2 and BLEU4. Overall, the correlation of RAINBOW Top5 can be classified as moderate, except for RNNLG and TGEN, where it correlates weakly with naturalness (0.35–0.38). While this model performs significantly worse than RAINBOW WBM+GBM and RAINBOW WBM, it significantly outperforms all of the single metrics, and
8
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
BAGEL SFHOTEL SFREST TGEN LOLS RNNLG LOLS RNNLG LOLS
RAINBOW WBM+GBM inform 0.71* 0.76* 0.71* 0.71* 0.71* 0.77* natural 0.74* 0.75* 0.79* 0.79* 0.73* 0.79* quality 0.71* 0.79* 0.79* 0.75* 0.72* 0.81* RAINBOW WBM inform 0.67* 0.75* 0.61* 0.67* 0.65* 0.72* natural 0.62* 0.73* 0.69* 0.77* 0.64* 0.72* quality 0.67* 0.74* 0.66* 0.75* 0.62* 0.77* RAINBOW GBM inform 0.57* 0.46* 0.51* 0.56* 0.52* 0.58* natural 0.49* 0.55* 0.57* 0.60* 0.54* 0.62* quality 0.50* 0.59* 0.54* 0.52* 0.54* 0.65* RAINBOW Top5 inform 0.59* 0.50* 0.47* 0.60* 0.54* 0.67* natural 0.35* 0.50* 0.36* 0.58* 0.38* 0.52* quality 0.41* 0.42* 0.40* 0.58* 0.39* 0.59*
Table 5: Spearman correlation between metrics and human ratings for combined models (‘*’ = p < 0.05).
Study/ task Sentence Planning Surface Realisation Domain this paper weak positive (ρ = 0.33, WPS) weak negative (ρ = 0.− 31, parser) NLG, restaurant/hotel search
(Reiter and Belz, 2009) none strong positive (Pearson’s r = 0.96, NIST) NLG, weather forecast (Stent et al., 2005) weak positive (ρ = 0.47, LSA) negative (ρ = −0.56, NIST) paraphrasing of news (Liu et al., 2016) weak positive (ρ = 0.35, BLEU-4) N/A dialogue/Twitter pairs
(Elliott and Keller, 2014) positive (ρ = 0.53, METEOR) N/A image caption (Kilickaya et al., 2017) positive (ρ = 0.64, SPICE) N/A image caption
Table 6: Best correlation results achieved by our and previous work.
it shows comparable performance to the RAINBOW GBM model, which combines more than twice as many features. However, in contrast to the RAINBOW GBM model, the Top5 model still needs costly human reference texts (each Top5 model contains at least one WBM). Thus, a promising future direction is reference-less quality prediction, as used in MT, e.g. (Specia et al., 2010).
10 Related Work
Table 6 summarises results published by previous studies in related fields investigating the relation between human scores and automatic metrics. These studies mainly considered WBMs, while we are the first study to consider GBMs. Some studies ask users to provide separate ratings for surface realisation (e.g. asking about ‘clarity’ or ‘fluency’), whereas other studies focus only on sentence planning (e.g. ‘accuracy’, ‘adequacy’, or ‘correctness’). In general, correlations reported by previous work range from weak to strong. The results confirm that correlations and metric performance appear to be system- and dataset-specific, with some results even directly opposed to each other, e.g. (Reiter and Belz, 2009) and (Stent et al., 2005). There is a general trend showing that bestperforming metrics tend to be the more complex ones, combining word-overlap, semantic similarity and term frequency weighting. (Note, however, that most previous works do not report whether any of the metric correlations are significantly dif-
ferent from each other.)
11 Conclusions and Future Directions
This paper shows that state-of-the-art automatic evaluation metrics for NLG systems do not sufficiently reflect human ratings. Word-based metrics make two strong assumptions: They treat humangenerated references as a gold-standard, which is correct and complete. We argue that these assumptions are invalid for corpus-based NLG, especially when using crowd-sourced datasets. Grammarbased metrics, on the other hand, do not rely on human-generated references and are not influenced by their quality. However, these metrics can be easily manipulated with grammatically-correct and easily-readable output that is unrelated to the input. To merge the advantages of WBMs and GBMs, we present a combined model, RAINBOW, which significantly improves correlation with human ratings.
In our future work, we will investigate more advanced metrics, as used in related fields, including: assessing output quality within the dialogue context, e.g. (Dušek and Jurčı́ček, 2016); extrinsic evaluation metrics, such as NLG’s contribution to task success, e.g. (Rieser et al., 2014; Gkatzia et al., 2016; Hastie et al., 2016); and referenceless quality prediction as used in MT, e.g. (Specia et al., 2010).
9
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
Appendix A: Detailed Results
BAGEL SFHOTEL SFREST
Inf: 0.16* Nat: 0.36* Qua: 0.38* Inf: 0.41* Nat: 0.47* Qua: 0.52* Inf: 0.35* Nat: 0.29* Qua: 0.35* TGEN: 0.42* LOLS: 0.24* RNNLG: 0.52* LOLS: 0.45* RNNLG: 0.42* LOLS: 0.24*
Total BAGEL: 0.31* Total SFHOTEL: 0.50* Total SFREST: 0.31* Total all data: 0.45*
Table 7: Intra-class correlation coefficient (ICC) for human ratings across the three datasets. “*” denotes statistical significance (p < 0.05).
BAGEL SFHOTEL SFREST TGEN LOLS RNNLG LOLS RNNLG LOLS
metric Avg / StDev Avg / StDev Avg / StDev Avg / StDev Avg / StDev Avg / StDev TER 0.36/0.24 0.33/0.24 0.28*/0.27 0.65*/0.32 0.41*/0.35 0.65*/0.27 BLEU1 0.75*/0.21 0.81*/0.16 0.85*/0.18 0.66*/0.23 0.73*/0.24 0.59*/0.23 BLEU2 0.68/0.23 0.72/0.21 0.78*/0.25 0.54*/0.28 0.62*/0.31 0.45*/0.29 BLEU3 0.60/0.28 0.63/0.26 0.69*/0.32 0.42*/0.33 0.52*/0.37 0.34*/0.33 BLEU4 0.52/0.32 0.53/0.33 0.56*/0.40 0.28*/0.33 0.44*/0.41 0.24*/0.32 ROUGE 0.76/0.18 0.78/0.17 0.83*/0.18 0.64*/0.21 0.72*/0.24 0.58*/0.22 NIST 4.44*/2.05 4.91*/2.04 4.37*/2.19 3.49*/1.99 4.86*/2.55 4.01*/2.07 LEPOR 0.46*/0.22 0.50*/0.19 0.52*/0.23 0.30*/0.16 0.51*/0.25 0.30*/0.17 CIDEr 2.92/2.40 3.01/2.27 3.08*/2.05 1.66*/1.67 3.39*/2.53 2.09*/1.73 METEOR 0.50/0.22 0.53/0.23 0.62*/0.27 0.44*/0.20 0.54*/0.28 0.41*/0.19 SIM 0.66/0.09 0.65/0.12 0.76*/0.15 0.73*/0.14 0.76/0.13 0.77/0.14 RE 86.79/19.48 83.39/20.41 70.90/17.07 69.62/19.14 64.67/19.07 64.27/22.22 msp 0.04*/0.21 0.14*/0.37 0.68/0.78 0.69/0.77 0.78/0.82 0.85/0.89 prs 84.51*/25.78 93.30*/27.04 97.58*/32.58 107.90*/36.41 93.74/34.98 97.20/39.30 len 38.20*/14.22 42.54*/14.11 49.06*/15.77 51.69*/17.30 53.27*/19.50 50.92*/18.74 wps 10.08*/3.10 10.94*/3.19 11.43*/3.63 12.07*/4.17 11.15*/4.37 10.52*/4.21 sps 13.15*/4.98 14.61*/5.13 16.03*/4.88 17.02*/5.90 16.39*/6.17 15.41*/5.92 cpw 3.77/0.60 3.88/0.59 4.34/0.58 4.36/0.63 4.86*/0.64 4.94*/0.76 spw 1.30/0.22 1.33/0.23 1.43/0.23 1.43/0.26 1.50/0.26 1.50/0.29 pol 2.22/1.21 2.40/1.16 1.24/1.04 1.33/1.04 1.69/1.12 1.57/1.07 ppw 0.22/0.09 0.22/0.09 0.11/0.10 0.12/0.09 0.16/0.11 0.16/0.12 informativeness 4.77/1.09 4.91/1.23 5.47*/0.81 5.27/1.02 5.29*/0.94 5.16/1.07 naturalness 4.76/1.26 4.67/1.25 4.99*/1.13 4.62/1.28 4.86/1.13 4.74/1.23 quality 4.77/1.19 4.54/1.28 4.54/1.18 4.53/1.26 4.51/1.14 4.58/1.33
Table 8: The systems’ performance for all datasets. Avg denotes a mean value, StDev stands for standard deviation, “*” denotes a statistically significant difference (p < 0.05) between the two systems on the given dataset.
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
B A
G E
L S
F H
O T
E L
S F
R E
S T
T G
E N
L O
L S
R N
N L
G L
O L
S R
N N
L G
L O
L S
m et
ri c
in f
na t
qu al
in f
na t
qu al
in f
na t
qu al
in f
na t
qu al
in f
na t
qu al
in f
na t
qu al
T E
R -0
.2 1*
-0 .1
9* -0
.1 6*
-0 .1
6* -0
.1 9*
-0 .1
6* -0
.0 3
-0 .0
9 -0
.0 8
-0 .0
6 -0
.2 0*
-0 .1
2* 0.
02 -0
.1 4*
-0 .0
8 -0
.1 6*
-0 .1
4* -0
.1 4*
B L
E U 1
0. 30
* 0.
15 *
0. 13
0. 13
0. 15
* 0.
13 0.
09 0.
09 *
0. 08
0. 01
0. 12
* 0.
06 0.
02 0.
12 *
0. 06
0. 19
* 0.
15 *
0. 13 * B L E U 2 0. 30 * 0. 17 * 0. 14 0. 12 0. 14 * 0. 11 0. 08 0. 09 * 0. 07 0. 00 0. 12 * 0. 07 0. 01 0. 13 * 0. 07 0. 14 * 0. 10 * 0. 08 * B L E U 3 0. 27 * 0. 17 * 0. 12 0. 11 0. 13 0. 10 0. 06 0. 08 0. 06 0. 01 0. 11 * 0. 08 0. 02 0. 13 * 0. 09 * 0. 12 * 0. 08 0. 07 B L E U 4 0. 23 * 0. 15 * 0. 11 0. 11 0. 13 0. 10 0. 06 0. 05 0. 07 0. 00 0. 02 0. 03 0. 03 0. 12 * 0. 07 0. 12 * 0. 04 0. 05 R O U G E 0. 20 * 0. 11 0. 09 0. 20 * 0. 17 * 0. 15 * 0. 07 0. 09 0. 08 -0 .0 1 0. 04 0. 02 0. 04 0. 17 * 0. 09 * 0. 12 * 0. 11 * 0. 08 N IS T 0. 24 * 0. 07 0. 02 0. 16 * 0. 13 0. 11 0. 07 0. 05 0. 01 0. 02 0. 14 * 0. 11 * 0. 03 0. 07 0. 01 0. 15 * 0. 08 0. 07 L E P O R 0. 17 * 0. 12 0. 07 -0 .0 7 0. 02 -0 .0 4 0. 03 0. 03 0. 03 0. 14 * 0. 17 * 0. 10 * 0. 00 0. 05 -0 .0 2 0. 28 * 0. 17 * 0. 18 * C ID E r 0. 26 * 0. 14 * 0. 10 0. 14 * 0. 19 * 0. 14 * 0. 07 0. 07 0. 00 0. 03 0. 13 * 0. 09 0. 02 0. 12 * 0. 03 0. 10 * 0. 11 * 0. 08 M E T E O R 0. 29 * 0. 09 0. 09 0. 20 * 0. 18 * 0. 16 * 0. 07 0. 10 * 0. 10 * 0. 05 0. 06 0. 04 0. 06 0. 16 * 0. 09 * 0. 23 * 0. 19 * 0. 17 * S IM 0. 16 * 0. 04 0. 06 0. 14 * 0. 13 0. 09 -0 .0 5 -0 .1 2* -0 .1 1* 0. 03 -0 .0 3 -0 .0 8 0. 13 * -0 .0 6 -0 .0 8* 0. 19 * 0. 01 0. 02 R E -0 .0 6 0. 09 0. 13 -0 .0 9 -0 .0 4 0. 04 0. 00 0. 03 0. 10 * -0 .0 1 0. 03 0. 09 0. 00 -0 .0 5 0. 02 0. 04 0. 09 * 0. 08 * cp w 0. 03 -0 .1 2 -0 .1 9* 0. 08 0. 05 -0 .0 3 0. 02 -0 .0 2 -0 .0 9* 0. 13 * 0. 14 * 0. 06 0. 02 0. 11 * 0. 01 0. 06 0. 10 * 0. 09 * le n 0. 25 * -0 .2 5* -0 .2 1* 0. 04 -0 .1 9* -0 .2 4* 0. 01 -0 .1 7* -0 .0 9 0. 12 * -0 .0 8 -0 .0 7 0. 11 * -0 .1 7* -0 .0 8 0. 21 * -0 .1 4* -0 .0 9* w ps 0. 33 * -0 .1 7* -0 .1 2 -0 .0 5 -0 .2 8* -0 .2 9* 0. 01 -0 .1 5* -0 .0 5 0. 08 -0 .1 2* -0 .0 8 0. 11 * -0 .1 9* -0 .0 7 0. 18 * -0 .1 5* -0 .1 1* sp s 0. 25 * -0 .2 0* -0 .1 7* 0. 03 -0 .1 7* -0 .2 3* -0 .0 2 -0 .1 6* -0 .0 8 0. 02 -0 .1 8* -0 .1 6* 0. 07 -0 .1 7* -0 .0 8 0. 12 * -0 .2 1* -0 .1 6* sp w 0. 01 -0 .0 7 -0 .1 3 0. 10 0. 09 0. 02 -0 .0 8 -0 .0 2 -0 .1 1* -0 .1 0* -0 .1 0* -0 .1 7* -0 .0 7 0. 06 -0 .0 3 -0 .1 4* -0 .1 0* -0 .1 1* po l 0. 16 * -0 .0 6 -0 .0 7 0. 11 -0 .0 3 -0 .1 2 -0 .0 7 -0 .1 0* -0 .1 5* 0. 01 -0 .0 9 -0 .1 4* -0 .0 4 -0 .0 4 -0 .0 3 -0 .0 2 -0 .1 3* -0 .1 1* pp w -0 .0 2 0. 06 0. 00 0. 16 * 0. 15 * 0. 08 -0 .0 9 -0 .0 6 -0 .1 6* -0 .0 2 -0 .0 1 -0 .0 9 -0 .0 9* 0. 08 0. 00 -0 .1 3* -0 .0 5 -0 .0 7 m sp -0 .0 2 -0 .0 6 -0 .1 1 0. 02 -0 .0 2 -0 .1 0 -0 .0 1 -0 .1 0* -0 .0 8 0. 05 -0 .0 2 -0 .0 3 0. 05 0. 02 -0 .0 6 0. 12 * 0. 01 0. 07 pr s 0. 23 * -0 .1 8* -0 .1 3 -0 .0 5 -0 .2 4* -0 .3 1* 0. 02 -0 .1 3* -0 .0 9 0. 13 * -0 .0 5 -0 .0 4 0. 11 * -0 .1 5* -0 .1 1* 0. 16 * -0 .2 0* -0 .1 6* Ta bl e 9: Sp ea rm an co rr el at io n be tw ee n m et ri cs an d hu m an ra tin gs fo ri nd iv id ua ld at as et s an d sy st em s. “* ” de no te s st at is tic al ly si gn ifi ca nt co rr el at io n (p < 0 .0 5) , bo ld fo nt de no te s si gn ifi ca nt ly st ro ng er co rr el at io n w he n co m pa ri ng tw o sy st em s on th e sa m e da ta se t.
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
BAGEL SFHOTEL SFREST inf nat qual inf nat qual inf nat qual
TER -0.19* -0.19* -0.15* -0.10* -0.19* -0.07* -0.09* -0.15* -0.08* BLEU1 0.23* 0.14* 0.11* 0.11* 0.18* 0.07* 0.11* 0.14* 0.07* BLEU2 0.21* 0.15* 0.12* 0.10* 0.17* 0.07* 0.09* 0.13* 0.06* BLEU3 0.19* 0.15* 0.11* 0.09* 0.16* 0.07* 0.08* 0.12* 0.06* BLEU4 0.18* 0.14* 0.10* 0.08* 0.10* 0.06 0.09* 0.09* 0.05 ROUGE 0.20* 0.13* 0.11* 0.09* 0.15* 0.06 0.09* 0.15* 0.06* NIST 0.21* 0.09 0.06 0.07* 0.13* 0.06 0.10* 0.08* 0.03 LEPOR 0.07 0.07 0.01 0.13* 0.15* 0.05 0.16* 0.12* 0.04 CIDEr 0.21* 0.16* 0.12* 0.10* 0.16* 0.05 0.08* 0.12* 0.04 METEOR 0.25* 0.13* 0.12* 0.11* 0.15* 0.08* 0.15* 0.18* 0.11* SIM 0.15* 0.09 0.07 0.01 -0.04 -0.09* 0.15* -0.02 -0.02 RE -0.08 0.03 0.09 0.01 0.04 0.10* 0.02 0.02 0.06 cpw 0.05 -0.04 -0.12* 0.07* 0.05 -0.02 0.04 0.10* 0.06 len 0.14* -0.22* -0.24* 0.05 -0.14* -0.07* 0.16* -0.15* -0.09* wps 0.14* -0.23* -0.23* 0.03 -0.14* -0.06 0.14* -0.17* -0.10* sps 0.14* -0.19* -0.21* -0.01 -0.18* -0.12* 0.10* -0.18* -0.12* spw 0.05 0.00 -0.06 -0.10* -0.06 -0.14* -0.11* -0.02 -0.07* pol 0.13* -0.05 -0.10* -0.04 -0.10* -0.14* -0.03 -0.08* -0.08* ppw 0.06 0.11* 0.04 -0.06 -0.04 -0.13* -0.11* 0.01 -0.04 msp 0.02 -0.04 -0.11* 0.02 -0.06 -0.06 0.08* 0.01 0.01 prs 0.10 -0.22* -0.25* 0.05 -0.12* -0.07 0.13* -0.18* -0.13*
Table 10: Spearman correlation between metrics and human ratings for each dataset. “*” denotes statistically significant correlation (p < 0.05).
TGEN LOLS RNNLG inf nat qual inf nat qual inf nat qual
TER -0.21* -0.19* -0.16* -0.07* -0.15* -0.11* -0.02 -0.13* -0.08* BLEU1 0.30* 0.15* 0.13 0.08* 0.12* 0.08* 0.07* 0.13* 0.07* BLEU2 0.30* 0.17* 0.14 0.05 0.11* 0.07* 0.06* 0.14* 0.08* BLEU3 0.27* 0.17* 0.12 0.04 0.09* 0.07* 0.06 0.13* 0.08* BLEU4 0.23* 0.15* 0.11 0.04 0.04 0.04 0.06 0.11* 0.08* ROUGE 0.20* 0.11 0.09 0.05 0.09* 0.05 0.07* 0.15* 0.09* NIST 0.25* 0.07 0.02 0.07* 0.11* 0.09* 0.04 0.06* 0.01 LEPOR 0.17* 0.12 0.07 0.13* 0.13* 0.11* 0.02 0.05 0.00 CIDEr 0.26* 0.14* 0.10 0.05 0.13* 0.09* 0.04 0.10* 0.02 METEOR 0.29* 0.09 0.09 0.14* 0.13* 0.12* 0.08* 0.15* 0.10* SIM 0.16* 0.04 0.06 0.14* 0.02 0.00 0.05 -0.08* -0.09* RE -0.06 0.09 0.13 -0.02 0.04 0.07* 0.02 -0.01 0.06* cpw 0.03 -0.12 -0.19* 0.11* 0.11* 0.08* -0.02 0.02 -0.05 len 0.25* -0.25* -0.21* 0.17* -0.12* -0.10* 0.06 -0.18* -0.08* wps 0.33* -0.17* -0.12 0.11* -0.17* -0.13* 0.07* -0.17* -0.06 sps 0.25* -0.20* -0.17* 0.09* -0.19* -0.17* 0.03 -0.17* -0.08* spw 0.01 -0.07 -0.13 -0.07* -0.06* -0.10* -0.09* 0.01 -0.07* pol 0.16* -0.06 -0.07 -0.02 -0.09* -0.11* -0.08* -0.08* -0.09* ppw -0.02 0.06 0.00 -0.08* 0.00 -0.05 -0.11* 0.00 -0.07* msp -0.02 -0.06 -0.11 0.10* 0.00 0.02 0.02 -0.04 -0.07* prs 0.23* -0.18* -0.13 0.12* -0.16* -0.15* 0.07* -0.14* -0.10*
Table 11: Spearman correlation between metrics and human ratings for each system. “*” denotes statistical significance (p < 0.05).
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
A cc
ur ac
y ra
nd T
E R
B L
E U 1
B L
E U 2
B L
E U 3
B L
E U 4
R O
U G
E N
IS T
L E
P O
R C
ID E r
M E
T E
R E
S IM
B A
G E
L
in fo
rm 37
.1 3
45 .0
5* 41
.5 8*
41 .5
8* 42
.5 7*
42 .0
8* 43
.0 7*
43 .0
7* 41
.5 8*
43 .0
7* 45
.5 4*
37 .1
3 41
.0 9*
na tu
ra l
42 .0
8 47
.0 3*
46 .0
4* 45
.5 4*
44 .0
6* 45
.0 5*
46 .0
4* 44
.5 5*
46 .5
3* 45
.0 5*
45 .0
5* 42
.0 8
43 .0 7* qu al ity 33 .1 7 45 .5 4* 43 .0 7* 40 .1 0* 40 .5 9* 43 .5 6* 43 .0 7* 41 .0 9* 40 .5 9* 42 .0 8* 41 .5 8* 37 .6 2 42 .5 7* S F H O T E L in fo rm 25 .3 8 34 .9 2* 35 .6 8* 35 .1 8* 35 .6 8* 34 .6 7* 36 .4 3* 31 .4 1 32 .1 6* 33 .9 2* 36 .4 3* 34 .9 2* 33 .9 2* na tu ra l 41 .9 6 45 .7 3 46 .4 8 45 .4 8 46 .4 8 45 .2 3 48 .7 4 41 .2 1 43 .7 2 44 .7 2 49 .7 5* 37 .1 9 46 .9 8 qu al ity 44 .4 7 40 .9 5 40 .9 5 42 .2 1 44 .7 2 41 .4 6 43 .2 2 40 .2 40 .9 5 42 .4 6 45 .9 8 33 .6 7 37 .4 4 S F R E S T in fo rm 33 .6 8 36 .2 7 35 .4 1 34 .0 2 34 .7 2 36 .9 6 33 .1 6 35 .5 8 36 .2 7 32 .4 7 34 .7 2 38 .3 4* 42 .6 6* na tu ra l 36 .1 0 40 .4 1 40 .0 7 38 .8 6 38 .3 4 38 .8 6 38 .1 7 39 .3 8 41 .1 1* 36 .7 9 39 .3 8 39 .3 8 38 .0 0 qu al ity 39 .3 8 37 .1 3 36 .9 6 39 .2 1 37 .6 5 39 .5 5 36 .1 0 38 .6 9 39 .7 2 35 .2 3 34 .8 9 40 .9 3 37 .3 1 S F R E S T ,q ua nt . in fo rm 31 .9 5 35 .7 5* 36 .2 7* 34 .3 7* 35 .9 2* 34 .5 4* 36 .4 4* 39 .5 5* 37 .1 3* 36 .2 7* 36 .7 9* 38 .1 7* 42 .8 3* qu al ity 39 .2 1 33 .3 3 34 .3 7 32 .3 30 .5 7 26 .9 4 34 .5 4 33 .1 6 35 .9 2 30 .9 2 31 .6 1 32 .4 7 35 .4 1 na tu ra ln es s 37 .1 3 37 .8 2 38 .6 9 36 .1 35 .7 5 32 .3 36 .9 6 39 .2 1 38 .8 6 35 .2 3 38 .3 4 34 .2 36 .1
Ta bl
e 12
:A cc
ur ac
y of
m et
ri cs
pr ed
ic tin
g re
la tiv
e hu
m an
ra tin
gs ,w
ith “*
” de
no tin
g st
at is
tic al
si gn
ifi ca
nc e
(p <
0. 05
).
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
informativeness naturalness quality Bad Good and avg Bad Good and avg Bad Good and avg
TER 0.48* 0.07* 0.31* 0.15* 0.08 0.06* BLEU1 0.45* 0.11* 0.26* 0.13* 0.07 0.04 BLEU2 0.49* 0.09* 0.29* 0.13* 0.05 0.04* BLEU3 0.40* 0.08* 0.25* 0.13* 0.01 0.05* BLEU4 0.41* 0.07* 0.21* 0.08* 0.01 0.04 ROUGE 0.50* 0.08* 0.28* 0.13* 0.07 0.04* NIST 0.26 0.08* 0.23* 0.08* 0.08 0.03 LEPOR 0.40* 0.09* 0.23* 0.10* 0.03 0.01 CIDEr 0.42* 0.09* 0.21* 0.12* 0.02 0.04 METEOR 0.45* 0.14* 0.24* 0.15* 0.03 0.08* SIM 0.37* 0.12* 0.29* -0.03 0.21* -0.08*
Table 13: Spearman correlation between WBM scores and human ratings for utterances from the Bad bin and utterances from the Good and Average bins. “*” denotes statistically significant correlation (p < 0.05), bold font denotes significantly stronger correlation for the Bad bin compared to the Good and Average bins.
informativeness naturalness quality Inform Not inform Inform Not inform Inform Not inform
TER -0.08* -0.10 -0.17* -0.18* -0.09* -0.11* BLEU1 0.11* 0.09 0.14* 0.20* 0.07* 0.11* BLEU2 0.09* 0.10 0.14* 0.20* 0.07* 0.13* BLEU3 0.07* 0.11* 0.13* 0.20* 0.06* 0.14* BLEU4 0.06* 0.11* 0.09* 0.18* 0.05* 0.14* ROUGE 0.08* 0.12* 0.14* 0.22* 0.06* 0.16* NIST 0.08* 0.05 0.10* 0.06 0.07* -0.06 LEPOR 0.09* 0.16* 0.11* 0.16* 0.05* 0.04 CIDEr 0.10* 0.01 0.16* 0.04 0.07* 0.02 METEOR 0.14* 0.17* 0.15* 0.22* 0.09* 0.18* SIM 0.15* 0.09 -0.01 -0.03 -0.05* -0.10 cpw 0.12* -0.15* 0.09* -0.14* 0.01 -0.11* len 0.17* 0.08 -0.15* -0.12* -0.12* -0.05 wps 0.11* 0.19* -0.19* -0.03 -0.12* 0.01 sps 0.09* 0.18* -0.20* -0.02 -0.17* 0.02 spw -0.06* 0.09 -0.03 0.01 -0.12* 0.01 pol -0.08* 0.05 -0.10* -0.03 -0.09* -0.03 ppw -0.14* -0.01 0.00 -0.03 -0.03 -0.05 msp 0.11* -0.03 0.00 -0.08 -0.03 -0.08 prs 0.10* 0.18* -0.18* -0.04 -0.15* -0.02
Table 14: Spearman correlation between automatic metrics and human ratings for utterances of the inform MR type and utterances of other MR types. “*” denotes statistically significant correlation (p < 0.05), bold font denotes significantly stronger (absolute) correlation for inform MRs compared to noninform MRs.

- Strengths:  The paper addresses a long standing problem concerning automatic evaluation of the output of generation translation systems.  The analysis of all the available metrics is thorough and comprehensive.  The authors demonstrate a new metric with a higher correlation with human judgements  The bibliography will help new entrants into the field.  - Weaknesses:  The paper is written as a numerical analysis paper, with very little insights to linguistic issues in generation, the method of generation, the differences in the output from a different systems and human generated reference.  It is unclear if the crowd source generated references serve well in the context of an application that needs language generation.  - General Discussion:  Overall, the paper could use some linguistic examples (and a description of the different systems) at the risk of dropping a few tables to help the reader with intuitions.


We present the first parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work, and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. To our knowledge, the conjunction of these formal properties is not supported by any existing parser. Our transition-based parser, which uses a novel transition set and features based on bidirectional LSTMs, has value not just for UCCA parsing: its ability to handle more general graph structures will inform the development of parsers for other semantic DAG structures, and in languages that frequently use discontinuous structures.
1 Introduction
Universal Conceptual Cognitive Annotation (UCCA, Abend and Rappoport, 2013) is a crosslinguistically applicable semantic representation scheme, building on the established Basic Linguistic Theory typological framework (Dixon, 2010a,b, 2012), and Cognitive Linguistics literature (Croft and Cruse, 2004). It has demonstrated applicability to multiple languages, including English, French, German and Czech, support for rapid annotation, and stability under translation (Sulem et al., 2015). It has also proven useful for machine translation evaluation (Birch et al., 2016). UCCA differs from syntactic schemes in terms of content and formal structure. It exhibits reentrancy, discontinuous nodes and non-terminals, which no single existing parser
supports. Lacking a parser, UCCA’s applicability has been so far limited, a gap this work addresses.
We present the first UCCA parser, TUPA (Transition-based UCCA Parser), building on recent advances in discontinuous constituency and dependency graph parsing, and further introducing novel transitions and features for UCCA. Transition-based techniques are a natural starting point for UCCA parsing, given the conceptual similarity of UCCA’s distinctions, centered around predicate-argument structures, to distinctions expressed by dependency schemes, and the achievements of transition-based methods in dependency parsing (Dyer et al., 2015; Andor et al., 2016; Kiperwasser and Goldberg, 2016). We are further motivated by the strength of transition-based methods in related tasks, including dependency graph parsing (Sagae and Tsujii, 2008; Ribeyre et al., 2014; Tokgöz and Eryiğit, 2015), constituency parsing (Sagae and Lavie, 2005; Zhang and Clark, 2009; Zhu et al., 2013; Maier, 2015; Maier and Lichte, 2016), AMR parsing (Wang et al., 2015a,b, 2016; Misra and Artzi, 2016; Goodman et al., 2016; Zhou et al., 2016; Damonte et al., 2017) and CCG parsing (Zhang and Clark, 2011; Ambati et al., 2015, 2016).
We evaluate TUPA on the English UCCA corpora, including in-domain and out-of-domain settings. To assess the ability of existing parsers to tackle the task, we develop a conversion procedure from UCCA to bilexical graphs and trees. Results show superior performance for TUPA, demonstrating the effectiveness of the presented approach.1
The rest of the paper is structured as follows: Section 2 describes UCCA in more detail. Section 3 introduces TUPA. Section 4 discusses the data and experimental setup. Section 5 presents
1Our code and models will be made freely available upon publication.
2
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
the experimental results. Section 6 summarizes related work, and Section 7 concludes the paper.
2 The UCCA Scheme
UCCA graphs are labeled, directed acyclic graphs (DAGs), whose leaves correspond to the tokens of the text. A node (or unit) corresponds to a terminal or to several sub-units (not necessarily contiguous) viewed as a single entity according to semantic or cognitive considerations. Edges bear a category, indicating the role of the sub-unit in the parent relation. Figure 1 presents a few examples.
UCCA is a multi-layered representation, where each layer corresponds to a “module” of semantic distinctions. UCCA’s foundational layer, targeted in this paper, covers the predicate-argument structure evoked by predicates of all grammatical categories (verbal, nominal, adjectival and others), the inter-relations between them, and other major linguistic phenomena such as coordination and multi-word expressions. The layer’s basic notion is the scene, describing a movement, action or state. Each scene contains one main relation (marked as either a Process or a State), as well as one or more Participants. For example, the sentence “After graduation, John moved to Paris” (Figure 1a) contains two scenes, whose main relations are “graduation” and “moved”. “John” is a Participant in both scenes, while “Paris” only in the latter. Further categories account for interscene relations and the internal structure of complex arguments and relations (e.g. coordination, multi-word expressions and modification).
One incoming edge for each non-root node is marked as primary, and the rest (mostly used for implicit relations and arguments) as remote edges, a distinction made by the annotator. The primary edges thus form a tree structure, whereas the remote edges enable reentrancy, forming a DAG.
While parsing technology in general, and transition-based parsing in particular, is wellestablished for syntactic parsing, UCCA has several distinct properties that distinguish it from syntactic representations, mostly UCCA’s tendency to abstract away from syntactic detail that do not affect argument structure. For instance, consider the following examples where the concept of a scene has a different rationale from the syntactic concept of a clause. First, non-verbal predicates in UCCA are represented like verbal ones, such as when they appear in copula clauses or noun phrases. Indeed,
(a) After
L
graduation P
H
, U
John
A
moved
P
to R
Paris
C
A
H
A
(b) John
A
gave
C
everything up
C
P
A P process A participant H linked scene C center R relator N connector L scene linker U punctuation F function unit
(c)
John
C
and
N
Mary
C
’s
F
A
trip
P
home
A
in Figure 1a, “graduation” and “moved” are considered separate events, despite appearing in the same clause. Second, in the same example, “John” is marked as a (remote) Participant in the graduation scene, despite not being overtly marked. Third, consider the possessive construction in Figure 1c. While in UCCA “trip” evokes a scene in which “John and Mary” is a Participant, a syntactic scheme would analyze this phrase similarly to “John and Mary’s children”.
These examples demonstrate that a UCCA parser, and more generally semantic parsers, face an additional level of ambiguity compared to their syntactic counterparts (e.g., “after graduation” is formally very similar to “after 2pm”, which does not evoke a scene). Section 6 discusses UCCA in the context of other semantic schemes, such as AMR (Banarescu et al., 2013).
Alongside recent progress in dependency parsing into projective trees, there is increasing interest in parsing into representations with more general structural properties (see Section 6). One such property is reentrancy, namely the sharing of semantic units between predicates. For instance, in Figure 1a, “John” is an argument of both “graduation” and “moved”, yielding a DAG rather than a tree. A second property is discontinuity, as in Figure 1b, where “gave up” forms a discontinuous semantic unit. Discontinuities are perva-
3
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
sive, e.g., with multi-word expressions (Schneider et al., 2014). Finally, unlike most dependency schemes, UCCA uses non-terminal nodes to represent units comprising more than one word. The use of non-terminal nodes is motivated by constructions with no clear head, including coordination structures (e.g., “John and Mary” in Figure 1c), some multi-word expressions (e.g., “The Haves and the Have Nots”), and prepositional phrases (either the preposition or the head noun can serve as the constituent’s head). To our knowledge, no existing parser supports all structural properties required for UCCA parsing.
3 Transition-based UCCA Parsing
We now turn to presenting TUPA. Building on previous work on parsing reentrancies, discontinuities and non-terminal nodes, we define an extended set of transitions and features that supports the conjunction of these properties.
Transition-based parsers (Nivre, 2003) scan the text from start to end, and create the parse incrementally by applying a transition at each step to the parser’s state, defined using three data structures: a buffer B of tokens and nodes to be processed, a stack S of nodes currently being processed, and a graph G = (V,E, `) of constructed nodes and edges, where V is the set of nodes, E is the set of edges, and ` : E → L is the label function, L being the set of possible labels. Some states are marked as terminal, meaning that G is the final output. A classifier is used at each step to select the next transition based on features encoding the parser’s current state. During training, an oracle creates training instances for the classifier, based on gold-standard annotations.
Transition Set. Given a sequence of tokens w1, . . . , wn, we predict a UCCA graph G over the sequence. Parsing starts with a single node on the stack (an artificial root node), and the input tokens in the buffer. Figure 2 shows the transition set.
In addition to the standard SHIFT and REDUCE operations, we follow previous work in transition-based constituency parsing (Sagae and Lavie, 2005), adding the NODE transition for creating new non-terminal nodes. For every X ∈ L, NODEX creates a new node on the buffer as a parent of the first element on the stack, with an Xlabeled edge. LEFT-EDGEX and RIGHT-EDGEX create a new primary X-labeled edge between the first two elements on the stack, where the parent is
the left or the right node, respectively. As a UCCA node may only have one incoming primary edge, EDGE transitions are disallowed if the child node already has an incoming primary edge. LEFTREMOTEX and RIGHT-REMOTEX do not have this restriction, and the created edge is additionally marked as remote. We distinguish between these two pairs of transitions to allow the parser to create remote edges without the possibility of producing invalid graphs. To support the prediction of multiple parents, node and edge transitions leave the stack unchanged, as in other work on transition-based dependency graph parsing (Sagae and Tsujii, 2008; Ribeyre et al., 2014; Tokgöz and Eryiğit, 2015). REDUCE pops the stack, to allow removing a node once all its edges have been created. To handle discontinuous nodes, SWAP pops the second node on the stack and adds it to the top of the buffer, as with the similarly named transition in previous work (Nivre, 2009; Maier, 2015). Finally, FINISH pops the root node and marks the state as terminal.
Classifier. The choice of classifier and feature representation has been shown to play an important role in transition-based parsing (Chen and Manning, 2014; Andor et al., 2016; Kiperwasser and Goldberg, 2016). To investigate the impact of the type of transition classifier in UCCA parsing, we experiment with three different models.
1. Starting with a simple and common choice (e.g., Maier and Lichte, 2016), TUPASparse uses a linear classifier with sparse features, trained with the averaged structured perceptron algorithm (Collins and Roark, 2004) and MINUPDATE (Goldberg and Elhadad, 2011): each feature requires a minimum number of updates in training to be included in the model.2
2. Changing the model to a feedforward neural network with dense embedding features, TUPAMLP (“multi-layer perceptron”), uses an architecture similar to that of Chen and Manning (2014), but with two rectified linear layers instead of one layer with cube activation. The embeddings and classifier are trained jointly.
3. Finally, TUPABiLSTM uses a bidirectional LSTM for feature representation, on top of the 2We also experimented with a linear model using dense embedding features, trained with the averaged structured perceptron algorithm. It performed worse than the sparse perceptron model and was hence discarded.
4
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
Before Transition Transition After Transition Condition Stack Buffer Nodes Edges Stack Buffer Nodes Edges Terminal? S x | B V E SHIFT S | x B V E − S | x B V E REDUCE S B V E − S | x B V E NODEX S | x y | B V ∪ {y} E ∪ {(y, x)X} − x 6= root S | y, x B V E LEFT-EDGEX S | y, x B V E ∪ {(x, y)X} −
x 6∈ w1:n, y 6= root, y 6;G x
S | x, y B V E RIGHT-EDGEX S | x, y B V E ∪ {(x, y)X} − S | y, x B V E LEFT-REMOTEX S | y, x B V E ∪ {(x, y)∗X} − S | x, y B V E RIGHT-REMOTEX S | x, y B V E ∪ {(x, y)∗X} − S | x, y B V E SWAP S | y x | B V E − i(x) < i(y) [root] ∅ V E FINISH ∅ ∅ V E +
Figure 2: The transition set of TUPA. We write the stack with its top to the right and the buffer with its head to the left. (·, ·)X denotes a primary X-labeled edge, and (·, ·)∗X a remote X-labeled edge. i(x) is a running index for the created nodes. In addition to the specified conditions, the prospective child in an EDGE transition must not already have a primary parent.
dense embedding features, an architecture similar to Kiperwasser and Goldberg (2016). The BiLSTM runs on the input tokens in forward and backward directions, yielding a vector representation that is then concatenated with dense features representing the parser state (e.g., existing edge labels and previous parser actions; see below). This representation is then fed into a feedforward network similar to TUPAMLP. The feedforward layers, BiLSTM and embeddings are all trained jointly.
For all classifiers, inference is performed greedily, i.e., without beam search. Hyperparameters are tuned on the development set (see Section 4).
Features. TUPASparse uses binary indicator features representing the words, POS tags, syntactic dependency labels and existing edge labels related to the top four stack elements and the next three buffer elements, in addition to their children and grandchildren in the graph. We also use bi- and trigram features based on these values (Zhang and Clark, 2009; Zhu et al., 2013), features related to discontinuous nodes (Maier, 2015, including separating punctuation and gap type), features representing existing edges and the number of parents and children, as well as the past actions taken by the parser. In addition, we use use a novel, UCCAspecific feature: number of remote children.3
For TUPAMLP and TUPABiLSTM, we replace all indicator features by a concatenation of the vector embeddings of all represented elements: words, POS tags, syntactic dependency labels, edge labels, punctuation, gap type and parser actions. These embeddings are initialized randomly. We additionally use external word embeddings initialized with pre-trained word2vec vectors (Mikolov
3See Appendix A for a full list of used feature templates.
et al., 2013),4 updated during training. In addition to dropout between NN layers, we apply word dropout (Kiperwasser and Goldberg, 2016): with a certain probability, the embedding for a word is replaced with a zero vector. We do not apply word dropout to the external word embeddings.
Finally, for all classifiers we add a novel realvalued feature to the input vector, ratio, corresponding to the ratio between the number of terminals to number of nodes in the graph G. This feature serves as a regularizer for the creation of new nodes, and should be beneficial for other transition-based constituency parsers too.
Training. For training the transition classifiers, we use a dynamic oracle (Goldberg and Nivre, 2012), i.e., an oracle that outputs a set of optimal transitions: when applied to the current parser state, the gold standard graph is reachable from the resulting state. For example, the oracle would predict a NODE transition if the stack has on its top a parent in the gold graph that has not been created, but would predict a RIGHT-EDGE transition if the second stack element is a parent of the first element according to the gold graph and the edge between them has not been created. The transition predicted by the classifier is deemed correct and is applied to the parser state to reach the subsequent state, if the transition is included in the set of optimal transitions. Otherwise, a random optimal transition is applied, and for the perceptronbased parser, the classifier’s weights are updated according to the perceptron update rule.
POS tags and syntactic dependency labels are extracted using spaCy (Honnibal and Johnson, 2015).5 We use the categorical cross-entropy ob-
4https://goo.gl/6ovEhC 5https://spacy.io
5
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
Parser state
S
,
B
John moved to Paris .
G
After
L
graduation
P
H
Transition classifier
After
LSTM
LSTM
LSTM
LSTM
graduation
LSTM
LSTM
LSTM
LSTM
to
LSTM
LSTM
LSTM
LSTM
Paris
LSTM
LSTM
LSTM
LSTM
. . .
. . .
. . .
. . .
. . .
MLP
NODEU
Figure 3: Illustration of the TUPA model. Top: parser state (stack, buffer and intermediate graph). Bottom: TUPABiLTSM architecture. Vector representation for the input tokens is computed by two layers of bidirectional LSTMs. The vectors for specific tokens are concatenated with embedding and numeric features from the parser state (for existing edge labels, number of children, etc.), and fed into the MLP for selecting the next transition.
jective function and optimize the NN classifiers with the Adam optimizer (Kingma and Ba, 2014).
4 Experimental Setup
Data. We conduct our experiments on the UCCA Wikipedia corpus (henceforth, Wiki), and use the English part of the UCCA Twenty Thousand Leagues Under the Sea English-French parallel corpus (henceforth, 20K Leagues) as outof-domain data.6 Table 1 presents some statistics for the two corpora. We use passages of indices up to 676 of the Wiki corpus as our training set, passages 688–808 as development set, and passages 942–1028 as in-domain test set. While UCCA edges can cross sentence boundaries, we adhere to the common practice in semantic parsing and train our parsers on individual sentences, discarding inter-relations between them (0.18% of the edges). We also discard linkage nodes and
6http://cs.huji.ac.il/˜oabend/ucca.html
edges (as they often express inter-sentence relations and are thus mostly redundant when applied at the sentence level) as well as implicit nodes.7 In the out-of-domain experiments, we apply the same parsers (trained on the Wiki training set) to the 20K Leagues corpus without parameter re-tuning.
Implementation. We use the DyNet package (Neubig et al., 2017) for implementing the NN classifiers. Unless otherwise noted, we use the default values provided by the package. See Appendix C for the hyperparameter values we found by tuning on the development set.
Evaluation. We define a simple measure for comparing UCCA structures Gp = (Vp, Ep, `p) and Gg = (Vg, Eg, `g), the predicted and goldstandard graphs, respectively, over the same sequence of terminals W = {w1, . . . , wn}. For an edge e = (u, v) in either graph, u being the parent and v the child, its yield y(e) ⊆ W is the set of terminals in W that are descendants of v. Define the set of mutual edges between Gp and Gg:
M(Gp, Gg) =
{(e1, e2) ∈ Ep × Eg | y(e1) = y(e2) ∧ `p(e1) = `g(e2)}
Labeled precision and recall are defined by dividing |M(Gp, Gg)| by |Ep| and |Eg|, respectively, and F-score by taking their harmonic mean. We report two variants of this measure: one where we consider only primary edges, and another for remote edges (see Section 2). Performance on remote edges is of pivotal importance in this investigation, which focuses on extending the class of graphs supported by statistical parsers.
7Appendix B further discusses linkage and implicit units.
6
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
After graduation , John moved to Paris
L U A
A
H
R
A
John gave everything up
A A
C
John and Mary went home
A
N
C
A
Figure 4: Bilexical graph approximation (dependency graph) for the sentences in Figure 1.
After
L
graduation P
H
, U
John
A
moved
P
to R
Paris
C
A
H
After graduation , John moved to Paris
L U A
H
R
A
Figure 5: Tree approximation (constituency) for the sentence in Figure 1a (top), and bilexical tree approximation (dependency) for the same sentence (bottom). These are identical to the original graphs, apart from the removal of remote edges.
We note that the measure collapses to the standard PARSEVAL constituency evaluation measure if Gp and Gg are trees. Punctuation is excluded from the evaluation, but not from the datasets.
Comparison to bilexical graph parsers. As no direct comparison with existing parsers is possible, we compare TUPA to bilexical dependency graph parsers, which support reentrancy and discontinuity but not non-terminal nodes. To facilitate the comparison, we convert our training set into bilexical graphs (see examples in Figure 4), train each of the parsers, and evaluate them by applying them to the test set and then reconstructing UCCA graphs, which are compared with the gold standard.8 In Section 5 we report the upper bounds on the achievable scores due to the error resulting from the removal of non-terminal nodes.
Comparison to tree parsers. For completeness, and as parsing technology is considerably more mature for tree (rather than graph) parsing, we also
8See Appendix D for the conversion procedures.
perform a tree approximation experiment, converting UCCA to (bilexical) trees and evaluating constituency and dependency tree parsers on them (see examples in Figure 5). Our approach is similar to the tree approximation approach used for dependency graph parsing (Agić et al., 2015; Fernández-González and Martins, 2015), where dependency graphs were converted into dependency trees and then parsed by dependency tree parsers. In our setting, the conversion to trees consists simply of removing remote edges from the graph, and then to bilexical trees by applying the same procedure as for bilexical graphs.
Baseline parsers. We evaluate two bilexical graph semantic dependency parsers: DAGParser (Ribeyre et al., 2014), the leading transition-based parser in SemEval 2014 (Oepen et al., 2014) and TurboParser (Almeida and Martins, 2015), a graph-based parser from SemEval 2015 (Oepen et al., 2015); UPARSE (Maier and Lichte, 2016), a transition-based constituency parser supporting discontinuous constituents; and two bilexical tree parsers: MaltParser (Nivre et al., 2007),9 and the stack LSTM-based parser of Dyer et al. (2015, henceforce “LSTM Parser”). Default settings are used in all cases. DAGParser and UPARSE use beam search by default, with a beam size of 5 and 4 respectively. The other parsers are greedy.
5 Results
Table 2 presents our main experimental results, as well as upper bounds for the baseline parsers, reflecting the error resulting from the conversion.
DAGParser and UPARSE are most directly comparable to TUPASparse, as they also use a perceptron classifier with sparse features. TUPASparse considerably outperforms both, where DAGParser does not predict any remote edges in the out-ofdomain setting. TurboParser fares worse in this comparison, despite somewhat better results on remote edges. The LSTM parser of Dyer et al. (2015) obtains the highest primary F-score among the baseline parsers, with a considerable margin.
Using a feedforward NN and embedding features, TUPAMLP obtains higher scores than TUPASparse, but is outperformed by the LSTM parser on primary edges. However, using better input encoding, TUPABiLSTM obtains substantially higher scores than TUPAMLP and all other parsers,
9For MaltParser we use the ARCEAGER transition set and SVM classifier. Other configurations yielded lower scores.
7
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
Wiki (in-domain) 20K Leagues (out-of-domain) Primary Remote Primary Remote
LP LR LF LP LR LF LP LR LF LP LR LF TUPASparse 64.5 63.7 64.1 19.8 13.4 16 59.6 59.9 59.8 22.2 7.7 11.5 TUPAMLP 65.2 64.6 64.9 23.7 13.2 16.9 62.3 62.6 62.5 20.9 6.3 9.7 TUPABiLSTM 74.4 72.7 73.5 47.4 51.6 49.4 68.7 68.5 68.6 38.6 18.8 25.3 Bilexical Approximation (Dependency DAG Parsers)
Upper Bound 91 58.3 91.3 43.4 DAGParser 61.8 55.8 58.6 9.5 0.5 1 56.4 50.6 53.4 – 0 0 TurboParser 57.7 46 51.2 77.8 1.8 3.7 50.3 37.7 43.1 100 0.4 0.8 Tree Approximation (Constituency Tree Parser)
Upper Bound 100 – 100 – UPARSE 60.9 61.2 61.1 – – – 52.7 52.8 52.8 – – – Bilexical Tree Approximation (Dependency Tree Parsers)
Upper Bound 91 – 91.3 – MaltParser 62.8 57.7 60.2 – – – 57.8 53 55.3 – – – LSTM Parser 73.2 66.9 69.9 – – – 66.1 61.1 63.5 – – –
Table 2: Experimental results, in percents, on the Wiki test set (left) and the 20K Leagues set (right). Columns correspond to labeled precision, recall and F-score, for both primary and remote edges. F-score upper bounds are reported for the conversions. For the tree approximation experiments, only primary edges scores are reported, as they are unable to predict remote edges. TUPABiLSTM obtains the highest F-scores in all metrics, surpassing the bilexical parsers, tree parsers and other classifiers.
on both primary and remote edges, both in the in-domain and out-of-domain settings. Its performance in absolute terms, of 73.5% F-score on primary edges, is encouraging in light of UCCA’s inter-annotator agreement of 80–85% F-score on them (Abend and Rappoport, 2013).
The parsers resulting from tree approximation are unable to recover any remote edges, as these are removed in the conversion.10 The bilexical DAG parsers are quite limited in this respect as well. While some of the DAG parsers’ difficulty can be attributed to the conversion upper bound of 58.3%, this in itself cannot account for their poor performance on remote edges, which is an order of magnitude lower than that of TUPABiLSTM.
6 Related Work
While earlier work on anchored11 semantic parsing has mostly concentrated on shallow semantic analysis, focusing on semantic role labeling of verbal argument structures, the focus has recently shifted to parsing of more elaborate representations that account for a wider range of phenomena.
10We also experimented with a simpler version of TUPA lacking REMOTE transitions, obtaining an increase of up to 2 labeled F-score points on primary edges, at the cost of not being able to predict remote edges.
11By anchored we mean that the semantic representation directly corresponds to the words and phrases of the text.
Grammar-Based Parsing. Linguistically expressive grammars such as HPSG (Pollard and Sag, 1994), CCG (Steedman, 2000) and TAG (Joshi and Schabes, 1997) provide a theory of the syntax-semantics interface, and have been used as a basis for semantic parsers by defining compositional semantics on top of them (Flickinger, 2000; Bos, 2005, among others). Depending on the grammar and the implementation, such semantic parsers can support some or all of the structural properties UCCA exhibits. Nevertheless, this line of work differs from our grammarless approach in two important ways. First, the representations are different. UCCA does not attempt to model the syntax-semantics interface and is thus less coupled with syntax. Second, while grammar-based parsers explicitly model syntax, grammarless approaches, as presented here, directly model the relation between tokens and semantic structures.
Broad-Coverage Semantic Parsing. Most closely related to this work is Broad-Coverage Semantic Dependency Parsing (SDP), addressed in two SemEval tasks (Oepen et al., 2014, 2015). Like UCCA parsing, SDP addresses a wide range of semantic phenomena, and supports discontinuous units and reentrancy. However, SDP uses bilexical dependencies, disallowing non-terminal nodes, useful for representing structures that have no clear head, such as co-
8
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
ordination (Ivanova et al., 2012). It also differs from UCCA in the type of distinctions it makes, which are more tightly coupled with syntactic considerations, where UCCA aims to capture purely semantic cross-linguistically applicable notions. For instance, the “poss” label in the DM target representation is used to annotate syntactic possessive constructions, regardless of whether they correspond to semantic ownership (e.g., “John’s dog”) or other semantic relations, such as marking an argument of a nominal predicate (e.g., “John’s kick”). UCCA reflects the difference between these constructions.
Recent interest in SDP has yielded numerous works on graph parsing (Ribeyre et al., 2014; Thomson et al., 2014; Almeida and Martins, 2015; Du et al., 2015), including tree approximation (Agić and Koller, 2014; Schluter et al., 2014) and joint syntactic/semantic parsing (Henderson et al., 2013; Swayamdipta et al., 2016).
Abstract Meaning Representation. Another line of work addresses parsing into AMRs (Flanigan et al., 2014; Vanderwende et al., 2015; Pust et al., 2015; Artzi et al., 2015), which, like UCCA, abstract away from syntactic distinctions and represent meaning directly, using OntoNotes predicates (Weischedel et al., 2013). Events in AMR may also be evoked by non-verbal predicates, including possessive constructions.
Unlike in UCCA, the alignment between AMR concepts and the text is not explicitly marked. While sharing much of this work’s motivation, not anchoring the representation in the text complicates the parsing task, as it requires the alignment to be automatically (and imprecisely) detected. Indeed, despite considerable technical effort (Flanigan et al., 2014; Pourdamghani et al., 2014; Werling et al., 2015), concept identification is only about 80%–90% accurate. Furthermore, anchoring allows breaking down sentences into semantically meaningful sub-spans, which is useful for many applications (Fernández-González and Martins, 2015; Birch et al., 2016).
Several transition-based AMR parsers have been proposed: CAMR assumes syntactically parsed input, processing dependency trees into AMR (Wang et al., 2015a,b, 2016; Goodman et al., 2016). In contrast, the parsers of Damonte et al. (2017) and Zhou et al. (2016) do not require syntactic pre-processing. Damonte et al. (2017) perform concept identification using a simple heuris-
tic selecting the most frequent graph for each token, and Zhou et al. (2016) perform concept identification and parsing jointly. UCCA parsing does not require separately aligning the input tokens to the graph. TUPA creates non-terminal units as part of the parsing process.
Furthermore, existing transition-based AMR parsers are not general DAG parsers. They are only able to predict a subset of reentrancies and discontinuities, as they may remove nodes before their parents have been predicted (Damonte et al., 2017). They are thus limited to a sub-class of AMRs in particular, and specifically cannot produce arbitrary DAG parses. TUPA’s transition set, on the other hand, allows general DAG parsing.12
7 Conclusion
We present TUPA, the first parser for UCCA. Evaluated in in-domain and out-of-domain settings, we show that coupled with a NN classifier and BiLSTM feature extractor, it accurately predicts UCCA graphs from text, outperforming a variety of strong baselines by a margin.
Despite the recent diversity of semantic parsing work, the effectiveness of different approaches for structurally and semantically different schemes is not well-understood (Kuhlmann and Oepen, 2016). Our contribution to this literature is a general grammarless parser that supports multiple parents, discontinuous units and non-terminal nodes.
Future work will explore different target representations and conversion procedures (Kong et al., 2015), to compare different representations, suggesting ways for a data-driven design of semantic annotation. A parser for UCCA will enable using the framework for new tasks, in addition to existing applications such as machine translation evaluation (Birch et al., 2016). We believe UCCA’s merits in providing a cross-linguistically applicable, broad-coverage annotation will support ongoing efforts to incorporate deeper semantic structures into various applications, such as sentence simplification (Narayan and Gardent, 2014) and summarization (Liu et al., 2015).

(the authors response answer most of the clarification questions of my review)  ========================= - Summary: =========================  The paper describes a transition-based system for UCCA graphs, featuring non-terminal nodes,  reentrancy and discontinuities. The transition set is a mix of already proposed transitions (The key aspects are the swap transition to cope with discontinuities, and transitions not popping the stack to allow multiple parents for a node.). The best performance is obtained using as transition classifier a MLP with features based on bidirectional LSTMs.  The authors compare the obtained performance with other state-of-the art parsers, using conversion schemes (to bilexical graphs, and to tree approximations): the parsers are trained on converted data, used to predict graphs (or trees), and the predicted structures are converted ack to UCCA and confronted with gold UCCA representations.  ========================= - Strengths: =========================  The paper presents quite solid work, with state-of-the art transition-based techniques, and machine learning for parsing techniques.  It is very well written, formal and experimental aspects are described in a very precise way, and the authors demonstrate a very good knowledge of the related work, both for parsing techniques and for shallow semantic representations.  ========================= - Weaknesses: =========================  Maybe the weakness of the paper is that the originality lies mainly in the targeted representations (UCCA), not really in the proposed parser.  ========================= - More detailed comments and clarification questions: =========================  Introduction  Lines 46-49: Note that  discontinuous nodes  could be linked to non-projectivity in the dependency framework. So maybe rather state that the difference is with phrase-structure syntax not dependency syntax.  Section 2:  In the UCCA scheme's description, the alternative  a node (or unit) corresponds to a terminal or to several sub-units  is not very clear. Do you mean something else than a node is either a terminal or a non terminal? Can't a non terminal node have one child only (and thus neither be a terminal nor have several sub-units) ?  Note that  movement, action or state  is not totally appropriate, since there are processes which are neither movements nor actions (e.g. agentless transformations). (the UCCA guidelines use these three terms, but later state the state process dichotomy, with processes being an  action, movement or some other relation that evolves in time ).  lines 178-181: Note that the contrast between  John and Mary's trip  and  John and Mary's children  is not very felicitous. The relational noun  children  evokes an underlying relation between two participants (the children and John+Mary), that has to be accounted for in UCCA too.  Section 4:  Concerning the conversion procedures: - While it is very complete to provide the precise description of the conversion procedure in the supplementary material, it would ease reading to describe it informally in the paper (as a variant of the constituent-to-dependency conversion procedure  u00e0 la Manning, 95). Also, it would be interesting to motivate the priority order used to define the head of an edge.  - How l(u) is chosen in case of several children with same label should be made explicit (leftmost ?).  - In the converted graphs in figure 4, some edges seem inverted (e.g. the direction between  John  and  moved  and between  John  and  gave  should be the same).  - Further, I am confused as to why the upper bound for remote edges in bilexical approximations is so low. The current description of the conversions do not allow to get an quick idea of which kind of remote edges cannot be handled.  Concerning the comparison to other parsers: It does not seem completely fair to tune the proposed parser, but to use default settings for the other parsers.  Section 5  Line 595: please better motivate the claim  using better input encoding   Section 6  I am not convinced by the alledged superiority of representations with non-terminal nodes. Although it can be considered more elegant not to choose a head for some constructions, it can be noted that formally co-head labels can be used in bilexical dependencies to recover the same information.