The success of a text simplification system heavily depends on the quality and quantity of complex-simple sentence pairs in the training corpus, which are extracted by aligning sentences between parallel articles. To evaluate and improve sentence alignment quality, we create two manually annotated sentence-aligned datasets from two commonly used text simplification corpora, Newsela and Wikipedia. We propose a novel neural CRF alignment model which not only leverages the sequential nature of sentences in parallel documents but also utilizes a neural sentence pair model to capture semantic similarity. Experiments demonstrate that our proposed approach outperforms all the previous work on monolingual sentence alignment task by more than 5 points in F1. We apply our CRF aligner to construct two new text simplification datasets, NEWSELA-AUTO and WIKI-AUTO , which are much larger and of better quality compared to the existing datasets. A Transformer-based seq2seq model trained on our datasets establishes a new state-of-the-art for text simplification in both automatic and human evaluation. 1 1 Introduction Text simplification aims to rewrite complex text into simpler language while retaining its original meaning (Saggion, 2017). Text simplification can provide reading assistance for children (Kajiwara et al., 2013), non-native speakers (Petersen and Ostendorf, 2007; Pellow and Eskenazi, 2014), nonexpert readers (Elhadad and Sutaria, 2007; Siddharthan and Katsos, 2010), and people with language disorders (Rello et al., 2013). As a preprocessing step, text simplification can also improve 1 Code and data are available at: https://github. the performance of many natural language processing (NLP) tasks, such as parsing (Chandrasekar et al., 1996), semantic role labelling (Vickrey and Koller, 2008), information extraction (Miwa et al., 2010) , summarization (Vanderwende et al., 2007; Xu and Grishman, 2009), and machine translation (Chen et al., 2012; Stajner and Popovic, 2016). Automatic text simplification is primarily addressed by sequence-to-sequence (seq2seq) models whose success largely depends on the quality and quantity of the training corpus, which consists of pairs of complex-simple sentences. Two widely used corpora, NEWSELA (Xu et al., 2015) and WIKILARGE (Zhang and Lapata, 2017), were created by automatically aligning sentences between comparable articles. However, due to the lack of reliable annotated data, 2 sentence pairs are often aligned using surface-level similarity metrics, such as Jaccard coefficient (Xu et al., 2015) or cosine distance of TF-IDF vectors (Paetzold et al., 2017), which fails to capture paraphrases and the context of surrounding sentences. A common drawback of text simplification models trained on such datasets is that they behave conservatively, performing mostly deletion, and rarely paraphrase (Alva-Manchego et al., 2017). Moreover, WIKILARGE is the concatenation of three early datasets (Zhu et al., 2010; Woodsend and Lapata, 2011; Coster and Kauchak, 2011) that are extracted from Wikipedia dumps and are known to contain many errors (Xu et al., 2015). To address these problems, we create the first high-quality manually annotated sentence-aligned datasets: NEWSELA-MANUAL with 50 article sets, and WIKI-MANUAL with 500 article pairs. We design a novel neural CRF alignment model, which utilizes fine-tuned BERT to measure semantic similarity and leverages the similar order of content be-2 Hwang et al. (2015) annotated 46 article pairs from Simple-Normal Wikipedia corpus; however, its annotation is noisy, and it contains many sentence splitting errors. tween parallel documents, combined with an effective paragraph alignment algorithm. Experiments show that our proposed method outperforms all the previous monolingual sentence alignment approaches (Stajner et al., 2018; Paetzold et al., 2017; Xu et al., 2015) by more than 5 points in F1. By applying our alignment model to all the 1,882 article sets in Newsela and 138,095 article pairs in Wikipedia dump, we then construct two new simplification datasets, NEWSELA-AUTO (666,645 sentence pairs) and WIKI-AUTO (488,332 sentence pairs). Our new datasets with improved quantity and quality facilitate the training of complex seq2seq models. A BERT-initialized Transformer model trained on our datasets outperforms the state-of-the-art by 3.4% in terms of SARI, the main automatic metric for text simplification. Our simplification model produces 25% more rephrasing than those trained on the existing datasets. Our contributions include: 1. Two manually annotated datasets that enable the first systematic study for training and evaluating monolingual sentence alignment; 2. A neural CRF sentence alinger and a paragraph alignment algorithm that employ fine-tuned BERT to capture semantic similarity and take advantage of the sequential nature of parallel documents; 3. Two automatically constructed text simplification datasets which are of higher quality and 4.7 and 1.6 times larger than the existing datasets in their respective domains; 4. A BERT-initialized Transformer model for automatic text simplification, trained on our datasets, which establishes a new state-of-the-art in both automatic and human evaluation. We propose a neural CRF sentence alignment model, which leverages the similar order of content presented in parallel documents and captures editing operations across multiple sentences, such as splitting and elaboration (see Figure 1 for an example). To further improve the accuracy, we first align paragraphs based on semantic similarity and vicinity information, and then extract sentence pairs from these aligned paragraphs. In this section, we describe the task setup and our approach. Given a simple article (or paragraph) S of m sentences and a complex article (or paragraph) C of n sentences, for each sentence s i ( i  [1 , m ] ) in the simple article, we aim to find its corresponding sentence c a i ( a i  [0 , n ] ) in the complex article. We use a i to denote the index of the aligned sentence, where a i = 0 indicates that sentence s i is not aligned to any sentence in the complex article. The full alignment a between article (or paragraph) pair S and C can then be represented by a sequence of alignment labels a = ( a 1 , a 2 , . . . , a m ) . Figure 1 shows an example of alignment labels. One spe-cific aspect of our CRF model is that it uses a varied number of labels for each article (or paragraph) pair rather than a fixed set of labels. We learn P ( a | S, C ) , the conditional probability of alignment a given an article pair ( S, C ) , using P ( a | S, C ) = exp(( a , S, C )) (cid:80) a A exp(( a , S, C )) = exp( (cid:80) | S | i =1  ( a i , a i  1 , S, C )) (cid:80) a A exp( (cid:80) | S | i =1  ( a i , a i  1 , S, C (1) where | S | = m denotes the number of sentences in article S . The score (cid:80) | S | i =1  ( a i , a i  1 , S, C ) sums over the sequence of alignment labels a = ( a 1 , a 2 , . . . , a m ) between the simple article S and the complex article C , and could be decomposed into two factors as follows:  ( a i , a i  1 , S, C ) = sim ( s i , c a i ) + T ( a i , a i  1 ) (2) where sim ( s i , c a i ) is the semantic similarity score between the two sentences, and T ( a i , a i  1 ) is a pairwise score for alignment label transition that a i follows a i  1 . Semantic Similarity A fundamental problem in sentence alignment is to measure the semantic similarity between two sentences s i and c j . Prior work used lexical similarity measures, such as Jaccard similarity (Xu et al., 2015), TF-IDF (Paetzold et al., 2017), and continuous n-gram features (Stajner et al., 2018). In this paper, we fine-tune BERT (De-vlin et al., 2019) on our manually labeled dataset (details in  3) to capture semantic similarity. Alignment Label Transition In parallel documents, the contents of the articles are often presented in a similar order. The complex sentence c a i that is aligned to s i , is often related to the complex sentences c a i  1 and c a i +1 , which are aligned to s i  1 and s i +1 , respectively. To incorporate this intuition, we propose a scoring function to model the transition between alignment labels using the following features: g 1 = | a i  a i  1 | g 2 = 1 ( a i = 0 , a i  1 (cid:54) = 0) g 3 = 1 ( a i (cid:54) = 0 , a i  1 = 0) g 4 = 1 ( a i = 0 , a i  1 = 0) (3) where g 1 is the absolute distance between a i and a i  1 , g 2 and g 3 denote if the current or prior sentence is not aligned to any sentence, and g 4 indicates whether both s i and s i  1 are not aligned to any sentences. where [ , ] represents concatenation operation and FFNN is a 2-layer feedforward neural network. We provide more implementation details of the model in Appendix A.1. During inference, we find the optimal alignment  a : using Viterbi algorithm in O ( mn 2 ) time. During training, we maximize the conditional probability of the gold alignment label a  : log P ( a  | S, C ) =( a  , S, C )  log (cid:88) a A exp(( a , S, C )) (6) The second term sums the scores of all possible alignments and can be computed using forward algorithm in O ( mn 2 ) time as well. Both accuracy and computing efficiency can be improved if we align paragraphs before aligning sentences. In fact, our empirical analysis revealed that sentence-level alignments mostly reside within the corresponding aligned paragraphs (details in  4.4 and Table 3). Moreover, aligning paragraphs first provides more training instances and reduces the label space for our neural CRF model. We propose Algorithm 1 and 2 for paragraph alignment. Given a simple article S with k paragraphs S = ( S 1 , S 2 , . . . , S k ) and a complex article C with l paragraphs C = ( C 1 , C 2 , . . . , C l ) , we first apply Algorithm 1 to calculate the semantic similarity matrix simP between paragraphs by averaging or maximizing over the sentence-level similarities (  2.2). Then, we use Algorithm 2 to generate the paragraph alignment matrix alignP . We align paragraph pairs if they satisfy one of the two conditions: (a) having high semantic similarity and appearing in similar positions in the article pair (e.g., both at the beginning), or (b) two continuous paragraphs in the complex article having relatively high semantic similarity with one paragraph in the simple side, (e.g., paragraph splitting or fusion). The difference of relative position in documents Algorithm 1: Pairwise Paragraph Similarity Initialize: simP  R 2  k  l to 0 2  k  l for i  1 to k do for j  1 to l do simP [1 ,i,j ] = avg s p  S i (cid:16) max c q  C j simSent ( s p ,c q ) (cid:17) simP [2 ,i,j ] = max s p  S i ,c q  C j simSent ( s p ,c q ) end endreturn simP Algorithm 2: Paragraph Alignment Algorithm Input: simP  R 2  k  l Initialize: alignP  I k  l to 0 k  l for i  1 to k do j max = argmax j simP [1 , i, j ] if simP [1 , i, j max ] >  1 and d ( i, j max ) <  2 then alignP [ i, j max ] = 1 endfor j  1 to l do if simP [2 , i, j ] >  3 then alignP [ i, j ] = 1 endif j > 1 & simP [2 , i, j ] >  4 & simP [2 , i, j  1] >  4 & d ( i, j ) <  5 & d ( i, j  1) <  5 then alignP [ i, j ] = 1 alignP [ i, j  1] = 1 end end endreturn alignP is defined as d ( i, j ) = | ik  jl | , and the thresholds  1  5 in Algorithm 2 are selected using the dev set. Finally, we merge the neighbouring paragraphs which are aligned to the same paragraph in the simple article before feeding them into our neural CRF aligner. We provide more details in Appendix A.1. To address the lack of reliable sentence alignment for Newsela (Xu et al., 2015) and Wikipedia (Zhu et al., 2010; Woodsend and Lapata, 2011), we designed an efficient annotation methodology to first manually align sentences between a few complex and simple article pairs. Then, we automatically aligned the rest using our alignment model trained on the human annotated data. We created two sentence-aligned parallel corpora (details in  5), which are the largest to date for text simplification. Newsela corpus (Xu et al., 2015) consists of 1,932 English news articles where each article (level 0) is re-written by professional editors into four simpler versions at different readability levels (level 1-4). We annotate sentence alignments for article pairs at adjacent readability levels (e.g., 0-1, 1-2) as the alignments between non-adjacent levels (e.g., 0-2) can be then derived automatically. To ensure efficiency and quality, we designed the following three-step annotation procedure: 1. Align paragraphs using CATS toolkit (Stajner et al., 2018), and then correct the automatic paragraph alignment errors by two in-house annotators. 3 Performing paragraph alignment as the first step significantly reduces the number of sentence pairs to be annotated from every possible sentence pair to the ones within the aligned paragraphs. We design an efficient visualization toolkit for this step, for which a screenshot can be found in Appendix E.2. 2. For each sentence pair within the aligned paragraphs, we ask five annotators on the Figure 3 We consider any sentence pair not in the aligned paragraph pairs as not-aligned . This assumption leads to a small number of missing sentence alignments, which are manually corrected in Step 3. Figure 2: Manual inspection of 100 random sentence pairs from our corpora (NEWSELA-AUTO and WIKIAUTO ) and the existing Newsela (Xu et al., 2015) and Wikipedia (Zhang and Lapata, 2017) corpora. Eight 4 crowdsourcing platform to classify into one of the three categories: aligned , partially-aligned , or not-aligned . We provide the annotation instructions and interface in Appendix E.1. We require annotators to spend at least ten seconds per question and embed one test question in every five questions. Any worker whose accuracy drops below 85% on test questions is removed. The inter-annotator agreement is 0.807 measured by Cohen's kappa (Artstein and Poesio, 2008). 3. We have four in-house annotators (not authors) verify the crowdsourced labels. We manually aligned 50 article groups to create the NEWSELA-MANUAL dataset with a 35/5/10 split for train/dev/test, respectively. We trained our aligner on this dataset (details in  4), then automatically aligned sentences in the remaining 1,882 article groups in Newsela (Table 1) to create a new sentence-aligned dataset, NEWSELA-AUTO , which consists of 666k sentence pairs predicted as aligned and partially-aligned . NEWSELA-AUTO is considerably larger than the previous NEWSELA (Xu et al., 2015) dataset of 141,582 pairs, and contains 44% more interesting rewrites (i.e., rephrasing and splitting cases) as shown in Figure 2. 4 We also create a new version of Wikipedia corpus by aligning sentences between English Wikipedia and Simple English Wikipedia. Previous work (Xu et al., 2015) has shown that Wikipedia is much noisier than the Newsela corpus. We provide this dataset in addition to facilitate future research. We first extract article pairs from English and Simple English Wikipedia by leveraging Wikidata, a well-maintained database that indexes named entities (and events etc.) and their Wikipedia pages in different languages. We found this method to be more reliable than using page titles (Coster and Kauchak, 2011) or cross-lingual links (Zhu et al., 2010; Woodsend and Lapata, 2011), as titles can be ambiguous and cross-lingual links may direct to a disambiguation or mismatched page (more details in Appendix B). In total, we extracted 138,095 article pairs from the 2019/09 Wikipedia dump, which is two times larger than the previous datasets (Coster and Kauchak, 2011; Zhu et al., 2010) of only 60  65k article pairs, using an improved version of the WikiExtractor library. 5 Then, we crowdsourced the sentence alignment annotations for 500 randomly sampled document pairs (10,123 sentence pairs total). As document length in English and Simple English Wikipedia articles vary greatly, 6 we designed the following annotation strategy that is slightly different from Newsela. For each sentence in the simple article, we select the sentences with the highest similarity scores from the complex article for manual annotation, based on four similarity measures: lexical similarity from CATS (Stajner et al., 2018), cosine similarity using TF-IDF (Paetzold et al., 2017), cosine similarity between BERT sentence embeddings, and alignment probability by a BERT model fine-tuned on our NEWSELA-MANUAL data (  3.1). As these four metrics may rank the same sentence at the top, on an average, we collected 2.13 complex sentences for every simple sentence and annotated the alignment label for each sentence pair. Our pilot study showed that this method captured 93.6% of the aligned sentence pairs. We named this manually labeled dataset WIKI-MANUAL with a train/dev/test split of 350/50/100 article pairs. Finally, we trained our alignment model on this 5 https://github.com/attardi/wikiextractor 6 The average number of sentences in an article is 9.2  16.5 for Simple English Wikipedia and 74.8  94.4 for English Wikipedia. annotated dataset to automatically align sentences for all the 138,095 document pairs (details in Appendix B). In total, we yielded 604k non-identical aligned and partially-aligned sentence pairs to create the WIKI-AUTO dataset. Figure 2 illustrates that WIKI-AUTO contains 75% less defective sentence pairs than the old WIKILARGE (Zhang and Lapata, 2017) dataset. In this section, we present experiments that compare our neural sentence alignment against the state-of-the-art approaches on NEWSELA-MANUAL (  3.1) and WIKI-MANUAL (  3.2) datasets. We compare our neural CRF aligner with the following baselines and state-of-the-art approaches: 1. Three similarity-based methods: Jaccard similarity (Xu et al., 2015), TF-IDF cosine similarity (Paetzold et al., 2017) and a logistic regression classifier trained on our data with lexical features from Stajner et al. (2018). 2. JaccardAlign (Xu et al., 2015), which uses Jaccard coefficient for sentence similarity and a greedy approach for alignment. 3. MASSAlign (Paetzold et al., 2017), which combines TF-IDF cosine similarity with a vicinity-driven dynamic programming algorithm for alignment. 4. CATS toolkit (Stajner et al., 2018), which uses character n-gram features for sentence similarity and a greedy alignment algorithm. We report Precision , Recall and F1 on two binary classification tasks: aligned + partially-aligned vs. not-aligned ( Task 1 ) and aligned vs. partially-aligned + not-aligned ( Task 2 ). It should be noted that we excluded identical sentence pairs in the evaluation as they are trivial to classify. Table 2 shows the results on NEWSELA-MANUAL test set. For similarity-based methods, we choose a threshold based on the maximum F1 on the dev set. Our neural CRF aligner outperforms the state-of-the-art approaches by more than 5 points in F1. In particular, our method performs better than the previous work on partial alignments, which contain many interesting simplification operations, such as sentence splitting and paraphrasing with deletion. Similarly, our CRF alignment model achieves 85.1 F1 for Task 1 ( aligned + partially-aligned vs. not-aligned ) on the WIKI-MANUAL test set. It outperforms one of the previous SOTA approaches CATS (Stajner et al., 2018) by 15.1 points in F1. We provide more details in Appendix C. 4.4 Ablation Study We analyze the design choices crucial for the good performance of our alignment model, namely CRF component, the paragraph alignment and the BERT-based semantic similarity measure. Table 3 shows the importance of each component with a series of ablation experiments on the dev set. CRF Model Our aligner achieves 93.2 F1 and 88.1 F1 on Task 1 and 2, respectively, which is around 3 points higher than its variant without the CRF component (BERT finetune + ParaAlign). Modeling alignment label transitions and sequential predictions helps our neural CRF aligner to handle sentence splitting cases better, especially when sentences undergo dramatic rewriting. Paragraph Alignment Adding paragraph alignment (BERT finetune + ParaAlign) improves the precision on Task 1 from 93.3 to 98.4 with a negligible decrease in recall when compared to not aligning paragraphs (BERT finetune ). Moreover, paragraph alignments generated by our algorithm (Our Aligner) perform close to the gold alignments (Our Aligner + gold ParaAlign) with only 0.9 and 0.3 difference in F1 on Task 1 and 2, respectively. Semantic Similarity BERT finetune performs better than other neural models, including Infersent (Conneau et al., 2017), ESIM (Chen et al., 2017), BERTScore (Zhang et al., 2020) and pre-trained BERT embedding (Devlin et al., 2019). For BERTScore, we use idf weighting, and treat simple sentence as reference. In this section, we compare different automatic text simplification models trained on our new parallel corpora, NEWSELA-AUTO and WIKI-AUTO , with their counterparts trained on the existing datasets. We establish a new state-of-the-art for sentence simplification by training a Transformer model with initialization from pre-trained BERT checkpoints. Existing datasets of complex-simple sentences, NEWSELA (Xu et al., 2015) and WIKILARGE (Zhang and Lapata, 2017), were aligned using lexical similarity metrics. NEWSELA dataset (Xu et al., 2015) was aligned using JaccardAlign (  4.1). WIKILARGE is a concatenation of three early datasets (Zhu et al., 2010; Woodsend and Lapata, 2011; Coster and Kauchak, 2011) where sentences in Sim-ple/Normal English Wikipedia and editing history were aligned by TF-IDF cosine similarity. For our new NEWSELA-AUTO , we partitioned the article sets such that there is no overlap between the new train set and the old test set, and vice-versa. Following Zhang and Lapata (2017), we also excluded sentence pairs corresponding to the levels 01, 12 and 23. For our WIKIAUTO dataset, we eliminated sentence pairs with high ( > 0.9) or low ( < 0.1) lexical overlap based on BLEU scores (Papineni et al., 2002), following Stajner et al. (2015). We observed that sentence pairs with low BLEU are often inaccurate paraphrases with only shared named entities and the pairs with high BLEU are dominated by sentences merely copied without simplification. We used the benchmark TURK corpus (Xu et al., 2016) for evaluation on Wikipedia, which consists of 8 human-written references for sentences in the validation and test sets. We discarded sentences in TURK corpus from WIKI-AUTO . Table 4 shows the statistics of the existing and our new datasets. We compare the following seq2seq models trained using our new datasets versus the existing datasets: 1. A BERT-initialized Transformer , where the encoder and decoder follow the BERT base architecture. The encoder is initialized with the same checkpoint and the decoder is randomly initialized (Rothe et al., 2020). 2. A randomly initialized Transformer with the same BERT base architecture as above. 3. A BiLSTM-based encoder-decoder model used in Zhang and Lapata (2017). 4. EditNTS (Dong et al., 2019), 7 a state-of-the-art neural programmer-interpreter (Reed and de Freitas, 2016) approach that predicts explicit edit operations sequentially. In addition, we compared our BERT-initialized Transformer model with the released system outputs from Kriz et al. (2019) and EditNTS (Dong et al., 2019). We implemented our LSTM and Transformer models using Fairseq. 8 We provide the model and training details in Appendix D.1. 7 https://github.com/yuedongP/EditNTS 8 https://github.com/pytorch/fairseq Evaluation on our new test set Evaluation on old test set SARI add keep del FK Len SARI add keep del FK Len Complex (input) 11.9 0.0 35.5 0.0 12 24.3 12.5 0.0 37.7 0.0 11 22.9 Models trained on old dataset (original NEWSELA corpus released in (Xu et al., 2015)) Transformer rand 33.1 1.8 22.1 75.4 6.8 14.2 34.1 2.0 25.5 74.8 6.7 14.2 LSTM 35.6 2.8 32.1 72.0 8.2 16.9 36.2 2.5 34.9 71.3 7.7 16.3 EditNTS 35.5 1.8 30.0 75.4 7.1 14.1 36.1 1.7 32.8 73.8 7.0 14.1 Transformer bert 34.4 2.4 25.2 75.8 7.0 14.5 35.1 2.7 27.8 74.8 6.8 14.3 Models trained on our new dataset (NEWSELA-AUTO ) Transformer rand 35.6 3.2 28.4 75.0 7.1 14.4 35.2 2.5 29.7 73.5 7.0 14.2 LSTM 35.8 3.9 30.5 73.1 7.0 14.3 36.4 3.3 33.0 72.9 6.6 14.0 EditNTS 35.8 2.4 29.4 75.6 6.3 11.6 35.7 1.8 31.1 74.2 6.1 11.5 Transformer bert 36.6 4.5 31.0 74.3 6.8 13.3 36.8 3.8 33.1 73.4 6.8 13.5 Simple (reference)     6.6 13.2     6.2 12.6 Table 5: Automatic evaluation results on NEWSELA test sets comparing models trained on our dataset NEWSELAAUTO against the existing dataset (Xu et al., 2015). In this section, we evaluate different simplification models trained on our new datasets versus on the old existing datasets using both automatic and human evaluation. We report SARI (Xu et al., 2016), Flesch-Kincaid ( FK ) grade level readability (Kincaid and Chissom, 1975), and average sentence length ( Len ). While SARI compares the generated sentence to a set of reference sentences in terms of correctly inserted, kept and deleted n-grams ( n  { 1 , 2 , 3 , 4 } ) , FK measures the readability of the generated sentence. We also report the three rewrite operation scores used in SARI: the precision of delete ( del ), the F1-scores of add ( add ), and keep ( keep ) operations. Wikipedia datasets respectively. Systems trained on our datasets outperform their equivalents trained on the existing datasets according to SARI. The difference is notable for Transformer bert with a 6.4% and 3.7% increase in SARI on NEWSELA-AUTO test set and TURK corpus, respectively. Larger size and improved quality of our datasets enable the training of complex Transformer models. In fact, Transformer bert trained on our new datasets outperforms the existing state-of-the-art systems for automatic text simplification. Although improvement in SARI is modest for LSTM-based models (LSTM and EditNTS), the increase in F1 scores for addition and deletion operations indicate that the models trained on our datasets make more meaningful changes to the input sentence. We also performed human evaluation by asking five Amazon Mechanical Turk workers to rate fluency, adequacy and simplicity (detailed instructions in Appendix D.2) of 100 random sentences generated by different simplification models trained on NEWSELA-AUTO and the existing dataset. Each SARI add keep del FK Len Complex (input) 25.9 0.0 77.8 0.0 13.6 22.4 Models trained on old dataset (WIKILARGE ) LSTM 33.8 2.5 65.6 33.4 11.6 20.6 Transformer rand 33.5 3.2 64.1 33.2 11.1 17.7 EditNTS 35.3 3.0 63.9 38.9 11.1 18.5 Transformer bert 35.3 4.4 66.0 35.6 10.9 17.9 Models trained on our new dataset (WIKI-AUTO ) LSTM 34.0 2.8 64.0 35.2 11.0 19.3 Transformer rand 34.7 3.3 68.8 31.9 11.7 18.7 EditNTS 36.4 3.6 66.1 39.5 11.6 20.2 Transformer bert 36.6 5.0 67.6 37.2 11.4 18.7 Simple (reference)     11.7 20.2 Table 8: Automatic evaluation results on Wikipedia TURK corpus comparing models trained on WIKIAUTO and WIKILARGE (Zhang and Lapata, 2017). worker evaluated these aspects on a 5-point Likert scale. We averaged the ratings from five workers. Table 7 demonstrates that Transformer bert trained on NEWSELA-AUTO greatly outperforms the one trained on the old dataset. Even with shorter sentence outputs, our Transformer bert retained similar adequacy as the LSTM-based models. Our Transformer bert model also achieves better fluency, adequacy, and overall ratings compared to the SOTA systems (Table 6). We provide examples of system outputs in Appendix D.3. Our manual inspection (Figure 3) also shows that Transfomer bert trained on NEWSELA-AUTO performs 25% more paraphrasing and deletions than its variant trained on the previous NEWSELA (Xu et al., 2015) dataset. Text simplification is considered as a text-to-text generation task where the system learns how to simplify from complex-simple sentence pairs. There is a long line of research using methods based on hand-crafted rules (Siddharthan, 2006; Niklaus et al., 2019), statistical machine translation (Narayan and Gardent, 2014; Xu et al., 2016; Wubben et al., 2012), or neural seq2seq models (Zhang and Lapata, 2017; Zhao et al., 2018; Nisioi et al., 2017). As the existing datasets were built using lexical similarity metrics, they frequently omit paraphrases and sentence splits. While training on such datasets creates conservative systems that rarely paraphrase, evaluation on these datasets exhibits an unfair preference for deletion-based simplification over paraphrasing. Sentence alignment has been widely used to extract complex-simple sentence pairs from parallel articles for training text simplification systems. Previous work used surface-level similarity metrics, such as TF-IDF cosine similarity (Zhu et al., 2010; Woodsend and Lapata, 2011; Coster and Kauchak, 2011; Paetzold et al., 2017), Jaccard-similarity (Xu et al., 2015), and other lexical features (Hwang et al., 2015; Stajner et al., 2018). Then, a greedy (Stajner et al., 2018) or dynamic programming (Barzilay and Elhadad, 2003; Paetzold et al., 2017) algorithm was used to search for the optimal alignment. Another related line of research (Smith et al., 2010; Tufis , et al., 2013; Tsai and Roth, 2016; Gottschalk and Demidova, 2017; Aghaebrahimian, 2018; Thompson and Koehn, 2019) aligns parallel sentences in bilingual corpora for machine translation. In this paper, we proposed a novel neural CRF model for sentence alignment, which substantially outperformed the existing approaches. We created two high-quality manually annotated datasets (NEWSELA-MANUAL and WIKI-MANUAL ) for training and evaluation. Using the neural CRF sentence aligner, we constructed two largest sentence-aligned datasets to date (NEWSELA-AUTO and WIKI-AUTO ) for text simplification. We showed that a BERT-initalized Transformer trained on our new datasets establishes new state-of-the-art performance for automatic sentence simplification. We thank three anonymous reviewers for their helpful comments, Newsela for sharing the data, Ohio Supercomputer Center (Center, 2012) and NVIDIA for providing GPU computing resources. We also thank Sarah Flanagan, Bohan Zhang, Raleigh Potluri, and Alex Wing for help with data annotation. This research is supported in part by the NSF awards IIS-1755898 and IIS-1822754, ODNI and IARPA via the BETTER program contract 19051600004, ARO and DARPA via the Social-Sim program contract W911NF-17-C-0095, Figure Eight AI for Everyone Award, and Criteo Faculty Research Award to Wei Xu. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of NSF, ODNI, IARPA, ARO, DARPA or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.
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