Parsing spoken dialogue poses unique diffi-culties, including disfluencies and unmarked boundaries between sentence-like units. Previous work has shown that prosody can help with parsing disfluent speech (Tran et al., 2018), but has assumed that the input to the parser is already segmented into sentence-like units (SUs), which isn't true in existing speech applications. We investigate how prosody affects a parser that receives an entire dialogue turn as input (a turn-based model ), instead of gold standard pre-segmented SUs (an SU-based model ). In experiments on the English Switchboard corpus, we find that when using transcripts alone, the turn-based model has trouble segmenting SUs, leading to worse parse performance than the SU-based model. However, prosody can effectively replace gold standard SU boundaries: with prosody, the turn-based model performs as well as the SU-based model (90.79 vs. 90.65 F1 score, respec-tively), despite performing two tasks (SU segmentation and parsing) rather than one (pars-ing alone). Following Tran et al. (2019) and others, we use a human-generated gold transcript instead of an automatic speech recognition RETRACTED This paper was retracted. Analysis shows that pitch and intensity features are the most important for this corpus, since they allow the model to correctly distinguish an SU boundary from a speech disfluency  a distinction that the model otherwise struggles to make. 1 Introduction Parsing spoken dialogue poses unique difficulties: spontaneous speech is full of disfluencies, including false starts, repetitions, and filled pauses. In addition, speech transcripts lack punctuation, which would otherwise help signal the boundaries of sentence-like units (SUs). 1 Because of these diffi-culties, current parsers struggle to accurately parse 1 We follow Kahn et al. (2004) in using the term sentence-like units' rather than sentences' throughout, since conversational speech doesn't always consist of syntactically complete sentences. English speech transcripts, even when they handle other English text well. However, research has shown that prosody can help with at least one of these problems, improving parsing performance for speech that contains disfluencies (Tran et al., 2018, 2019). In this work, we hypothesize that incorporating prosodic features from the speech signal can actually help with both of these problems: not only parsing disfluent speech, but also parsing speech that isn't segmented into SUs. Other researchers have augmented parsers with prosodic features, but always with the assumption that the parser has access to gold SU boundaries, which cannot be assumed in a deployed speech application. For example, Gregory et al. (2004); Kahn et al. (2005) and Hale et al. (2006) incorporated prosody into statistical parsers or parse rerankers, with mixed results. More recently, Tran et al. (2018) and Tran et al. (2019) found that prosody improved an end-to-end neural parser, with the most significant gains in disfluent sentences. Parsing without access to gold SU boundaries is much more difficult: Kahn and Ostendorf (2012) showed that parsing quality depends on the quality of the sentence segmentation. Furthermore, finding SU boundaries is not as simple as finding long pauses in speech, as we demonstrate below. We hypothesize that access to prosodic features will help an English parser that has to both parse and correctly identify SU boundaries (which we call SU segmentation ). We test this hypothesis by inputting entire dialog turns to a neural parser without gold SU boundaries. We call this the turn-based model, and compare it to an SU-based model, which assumes gold SU boundaries and parses one SU at a time. We use turns as our input unit because they resemble the input a dialog agent would receive from a user. 980 (ASR) transcript; we plan to use ASR output in future work. We build on the work of Tran et al. (2018) and Tran et al. (2019), considering two different experimental conditions for each model: inputting text features only and inputting both text and prosodic features. Using the Switchboard corpus of English conversational dialogue, we find that when only transcripts are used, the turn-based parser performs considerably worse than the SU-based parser, which is not surprising given that it needs to perform two tasks instead of one. However, when prosodic features are included, there is no difference in performance between the turn-based and SU-based models, and both models outperform the text-only counterparts. Our primary contributions are:  We show that a parser that has access to prosody can perform both SU segmentation and parsing as well as a model that only has to parse. We show that one difficultly for the prosody-free turn-based model is that it confuses speech disfluencies with SU boundaries, as illustrated in Figure 1. Further analysis indicates that adding pitch and intensity features can help the model to disambiguate the two, while pause and duration features do not. RETRACTED This paper was retracted. 2 Background: prosody and syntax Prosodic signals divide speech into units (Pier-rehumbert, 1980). The location and type of these prosodic units are determined by information structure (Steedman, 2000), disfluencies (Shriberg, 2001), and to some extent, syntax (Cutler et al., 1997). Some psycholinguistic research shows that in experimental conditions, speakers can use prosody to predict syntax  for example, that English speakers can use prosody to determine where to attach a modifier or prepositional phrase, or how to correctly group coordinands (e.g., Kjelgaard and Speer (1999); Speer et al. (1996); Warren et al. (1995)). However, Cutler et al. (1997) argues that English speakers often fail to exploit this prosodic information even when it is present, so it isn't actually a signal for syntax in practice. Many computational linguists have experimented with this possible link between syntax and prosody by incorporating prosody into syntactic parsers (e.g., Noeth et al. (2000); Gregory et al. (2004); Kahn et al. (2005); Tran et al. (2018)). These models have had mixed success: For example, Gregory et al. (2004) found that prosody was at best a neutral addition to their model, while Kahn et al. (2005) found that prosody helped rerank PCFG output. One possible reason that prosody is only somewhat effective in previous research is that prosodic units below the level of the SU do not always coincide with traditional syntactic constituents (Selkirk, 1995, 1984). 2 In fact, the only prosodic boundaries that consistently coincide with syntactic boundaries are the prosodic boundaries at the ends of SUs (Wagner and Watson, 2010). The prosodic boundaries at the end of SUs are more distinctive (i.e., tending to correspond to longer pauses and more distinctive pitch and intensity variations) and less likely appear in any other location. These features make prosody a reliable signal for SU boundaries, even though it is an unreliable signal for syntactic structure below the SU level. Some researcheres have used this correlation between prosody and SU boundaries to help in SU boundary detection. Examples of SU segmentation models that found prosodic cues were important include Gotoh and Renals (2000); Kolar et al. (2006); Kahn et al. (2004); Kahn and Ostendorf (2012), who all used traditional statistical models (e.g., HMMs, finite state machines, and decision trees), and Xu et al. (2014), who used a neural model. Kahn et al. (2004) and Kahn and Ostendorf (2012) also looked at downstream parsing accuracy on the same corpus we use. Like us, Kahn and Ostendorf (2012) don't use gold SU boundaries, but direct comparison is impossible because they use ASR output instead of human transcriptions and a different metric for parse performance (SPar-seval; Roark et al. (2006)). However, they show that having access to gold SU boundaries increases the SParseval score from 78.5 to 82.3, which shows that parsing without gold SU boundaries is difficult. However, in some research areas, prosody is less frequently used for SU detection. Some ASR corpora and applications segment at relatively arbitrary boundaries such as long silences or even regular intervals (e.g., Jain et al. (2020)). Other applications, such as speech translation, do require syntactically coherent input, but even there, systems targeting SUs have often used only textual features (Sridhar et al., 2013; Wan et al., 2020). 2 We refer here to traditional constituency parsing; CCG (Steedman and Baldridge, 2011) proposes different syntactic constituents that coincide with prosodic units. TURN S { th} that's of course being facetious SBARQ { how do you } how do you feel (a) Text+prosody model output TURN SQ do you feel { th} that's of course being facetious WHADVP how EDITED { how do you } disfluencies (shown in curly braces) and an SU boundary. (a), which matches the gold SU boundaries. (b)). Systems for restoring punctuation from ASR output must identify SU boundaries to correctly insert sentence-final punctuation, but these systems are typically evaluated on rehearsed monologues (such as TED talks) or read speech, which largely lack disfluencies (e.g., Federico et al. (2012)). Here, we show that prosody is primarily helpful for distinguishing SU boundaries from disfluencies, so although some of these systems have used prosody (e.g., Tilk and Alumae (2016)), text-only systems are very competitive (e.g., Che et al. (2016); Alam et al. (2020)). Even when SU boundaries are already known, other research in parsing conversational speech has shown that prosody helps identify and correctly handle disfluencies. Tran et al. (2018) found that prosody only modestly affects parsing of fluent SUs, but has a marked effect on disfluent SUs. This accords with other previous work that has found that prosody is helpful in disfluency detection (Zayats and Ostendorf, 2019) We discuss the relationship between prosody and disfluencies in greater detail in Section 6, including how prosody helps the model not to confuse disfluencies and SU boundaries, as shown in Figure 1 above. This example shows how the two sentences in (1) would be fused RETRACTED This paper was retracted. We use the American English corpus Switchboard NXT (henceforth SWBD-NXT) (Calhoun et al., 2010). We choose this corpus mainly so we can compare performance with Tran et al. (2018) and Tran et al. (2019), as well as other earlier probabilistic models such as Kahn et al. (2005). SWBD-NXT comprises 642 dialogues between strangers conducted by telephone. These dialogues are transcribed and hand-annotated with Penn Treebank-style constituency parses. We preprocess the transcripts to remove punctuation and lower-case all letters, making the input more like an ASR transcript that would be used in a deployed application. The transcript divides the corpus into SUs and turns . Since these SUs may be sentences or other syntactically independent units such as sentence fragments, we use the generic term sentence-like unit' (SU). A turn is a contiguous span of speech by a single speaker. Turns are hand-annotated in SWBD-NXT, but for a deployed dialog agent, a turn is simply whatever contiguous input the user gives. Not all turns in the SWBD-NXT contain more than one SU: of a total 60.1k turns, 35.8k consist of a single SU. The remaining 24.3k contain more than one SU; the majority (52.4 percent) of these contain just two SUs. The average number of SUs per turn is 1.82. We follow the general approach of Tran et al. (2018), but where they parse a single SU at a time, we give our parser a single dialog turn at a time for our turn-based model. The model returns constituency parses for the turn in the form of Penn Treebank (PTB)-style trees. In order to keep the output in the form of valid PTB trees, we add a top-level constituent, labelled TURN , to all turns, however many SUs they consist of. (1) Separate SUs: a. ( S ( NP Kim) ( VP sings)) b. ( S ( NP Sidney) ( VP dances)) (2) Merged into a single turn: a. ( TURN ( S ( NP Kim) ( VP sings)) ( S ( NP Sidney) ( VP dances))) Of course, using turns instead of SUs leads to longer inputs. We experiment with a pipeline approach (first segmenting turns into SUs, then parsing) as well as an end-to-end approach. In the end-to-end approach, we can't handle extremely long inputs since these longer sequences lead to high memory usage for transformers. We still want to capture the model's behavior on generally longer inputs, so we filter out two problematically long turns from the training set (out of 49,294 turns). We do not have to remove any turns from the development or test sets. This leaves the maximum turn length at 270 tokens. We also remove any turns for which some or all speech features are missing from the corpus. From the speech signal, we extract features for pauses between words, word duration, pitch, and intensity. We largely follow the feature extraction procedure outlined in Tran et al. (2018) and Tran et al. (2019), which we summarize here, noting any deviations from or additions to their procedure. 3 The model is a neural end-to-end constituency 3 Original: https://github.com/trangham283/prosody nlp; our extended code: https://github.com/ekayen/prosody nlp RETRACTED This paper was retracted. Pause features are extracted from the time-aligned transcript. Each word's pause feature corresponds to the pause follows it. Each pause is categorized into one of six bins by length in seconds: p > 1 , 0 . 2 < p  1 , 0 . 05 < p  0 . 2 , 0 < p  0 . 05 , p  0 (see below), and pauses where we are missing time-aligned data. Following Tran et al. (2018), the model learns 32-dimensional embeddings for each pause category. Since we use turns instead of SUs, we have to determine how to handle pauses at the beginnings and endings of turns. We decide to calculate pauses based on all words in the transcript, not just the words for a single speaker at a time. This means that at a turn boundary, we calculate the pause as the time between the end of one speaker's turn and the beginning of the other speaker's turn. If one speaker interrupts another, the pause duration has a negative value. We place these negative-valued pauses in the same bin as pauses with length 0. Duration features are also extracted from the time-aligned transcript. We are interested in the relative lengthening or shortening of word tokens, so we normalize the raw duration of each token. Following the code base for Tran et al. (2019), we perform two different types of normalization. In the first case, we normalize the token's raw duration by the mean duration of every instance of that word type. In the second, we normalize the token's raw duration by the maximum duration of any word in the input unit (SU or turn). These two normalization methods result in two duration features for each word token, which are concatenated and input to the model. Pitch features (or more accurately, F0 features) are extracted from the speech signal using Kaldi (Povey et al., 2011). These are extracted from 25ms frames every 10ms. Three pitch features are extracted: warped Normalized Cross Correlation Function (NCCF); log-pitch with mean subtraction over a 1.5-second window, weighted by Probability of Voicing (POV); and the estimated derivative of the raw log pitch. For further details on these features, see Ghahremani et al. (2014). Intensity features are also extracted from the speech signal using the same software and frame size as we use for pitch features. Starting with 40-dimensional mel-frequency filterbank features, we calculate three features: (1) the log of the total energy, normalized by the maximum total energy for the speaker over the course of the dialog; (2) the log of the total energy in the lower half of the 40 mel-frequency bands, normalized by the total energy; and (3) the log of the total energy in the upper half of the 40 mel-frequency bands, normalized by the total energy. For training, development, and testing, we use the split described in Charniak and Johnson (2001), which is a standard split for experiments on SWBD-NXT (e.g., Kahn et al. (2005); Tran et al. (2018)). The training set makes up 90 percent of the data, and the development and testing sets make up 5 percent each. We use the parser described in Tran et al. (2019), directly extending the code base described in their paper. parser based on Kitaev and Klein (2018)'s text-only parser, with a transformer-based encoder and a chart-style decoder based on Stern et al. (2017) and Gaddy et al. (2018). This encoder-decoder is augmented with a CNN on the input side that handles prosodic features (Tran et al., 2019). For further description of the model and hyperparameters, see Appendices A.1 and A.2. The text is encoded using 300-dimensional GloVe embeddings (Pennington et al., 2014). 4 Of the four types of prosodic features described in Section 3, pause and duration features are already token-level. However, pitch and intensity features are extracted from the speech signal at the frame level. In order to map from these frame-level features to a token-level representation, the pitch and intensity features pass through a CNN, and are then concatenated with the token-level pause and duration features. We follow Tran et al. (2019) in training each model 10 times with different random seeds. For the development set, we report the mean of these 10 models' performance. We then select the median model by development set performance, and use it to calculate test set results. For any further experiments, such as those discussed in Section 6, we use the random seed for this median model. Each model is trained for 50 epochs and use the epoch with highest development set performance. The turn-based parser's task is also more com-R ETRACTED This paper was retracted. In addition to this end-to-end approach, we also report results for a pipeline approach. For the pipeline, we first segment the speech into SUs using a modified version of the parser architecture: We keep the encoder the same, but we change the decoder so that it only does sequence labelling, and we frame the SU segmentation task as a sequence labelling task. We then use the SU-based parser to parse the resulting SUs. We report the model's performance with and without prosodic features during the segmentation and parsing steps. 5 Results We compare the turn-based F1 performance of our parser to a replication of the SU-based performance described in Tran et al. (2018) and Tran et al. (2019). Table 1 shows the development and test set results. 5 We find that the turn-based model benefits significantly from prosody. The turn-based 4 See Appendix A.3 for results using BERT embeddings. 5 We use PyEvalb to evaluate our parser's performance, though we modify it so that it behaves identically to Evalb: https://github.com/ekayen/PYEVALB SU-based Turn-based Test set: Text only 90.29 86.56 Text+prosody 90.65 90.79 Dev. model performs equivalently well to the SU-based model, despite doing two tasks instead of one. The SU-based model also improves by 0.36 in F1 score on the test set with the addition of prosody. Note that while prosody has a considerably larger effect on the turn-based model than on the SU based model, the exact size of this change will depend on the corpus. For example, in a corpus with very few multi-SU turns, the performance change in the turn-based model might not be as large. However, our results suggest that prosody helps when a model needs to both detect SU boundaries and parse SUs. The biggest difference between the SUand turn-based models' performance on this corpus is in the text-only scenario, where the turn-based parser is substantially worse. This is expected for a few reasons. First, the text-only turn-based parser encounters longer inputs. Longer inputs tend to lead to more parse errors simply because there are more ways to parse a longer string. Table 2 shows this correspondence between length and performance. The median length of turns in the development set is 9 tokens, while the median length of SUs is 6 tokens. Longer strings are also more likely to contain the things that make parsing difficult, namely disfluencies and SU boundaries. This gives the turn-based parser novel ways to make errors by splitting a turn into the wrong number of SUs. However, prosody brings the turn-based parser up to the level of the SU-based parser, even though the turn-based model's task is more complex. Table 5 shows how the text-only parser significantly overestimates the number of SU boundaries. Without prosody, the model achieves an F1 score of 63.74 on SU prediction on the development set, compared to 99.41 with prosody (see Table 3). The most comparable work on SWBD is Kahn and Ostendorf (2012), who achieved 78 F1 using a hidden-event model, where we use a much more powerful transformer model; however, their model used ASR transcripts as input, so these scores aren't directly comparable. RETRACTED This paper was retracted. We also test the pipeline model described in Section 4, which first segments turns into SUs and then parses them, both with and without prosody. We train just one segmentation model with the same random seed as the median development set model. We report the development set performance on segmentation (measured by segmentation F1 (Makhoul et al., 2000)) and parse F1 in Table 3. The text+prosody pipeline model achieves an F1 score of 99.71, which is statistically indistinguishable from the end-to-end text+prosody model. In both cases, we see that the addition of prosody boosts SU segmentation accuracy to near-perfect levels, which explains why the parser performance is similar (and much better than without prosody). Comparing the two text-only models reveals a more interesting pattern: while the pipeline model achieves much better segmentation F1, its parsing performance is worse. This is unexpected, as parsing and segmentation performance are usually correlated. This effect seems to arise because the two models err in different directions on segmentation: The pipeline model under-segments turns (corre-sponding to higher segmentation precision), while the end-to-end over-segments (higher recall, substantially lower precision). When it over-segments, the end-to-end text-only model often splits a word or short constituent off of an otherwise well-formed SU subtree; by contrast, the pipeline model tends to leave two or more SUs combined and and then to generate many SU-internal parsing errors. These SU-internal parsing errors include more coordination errors as well as VP, NP, and clause attachment errors than the end-to-end model. 6 However, the pipeline model does as well as the end-to-end model at PP attachment and modifier attachment. Overall, these results show that a pipeline model can be as effective at parsing as an end-to-end one, but that including prosody is even more important for a pipeline model. Since we care about parsing performance and the end-to-end text-only model does much better at parsing, we use the end-to-end model for all remaining analyses. We use the Berkeley Parser Analyser (Kummer-feld et al., 2012) to determine what types of errors each of the SU-based and end-to-end turn-based models makes. Figure 2 summarizes the output of the Analyser. Overall, the SU-based parser shows only small effects from prosody, but the turn-based model does significantly worse on certain error types without prosody. Even for the turn-based model, prosody only affects error types that have to do with the shape of the tree. The different label category shows errors where two identically shaped trees have different constituent labels, and prosody has no effect on these. For the turn-based model, poor SU segmentation by the text-only model explains some of the differences between the text+prosody and text-only models. Since 68.8 percent of SUs are clauses (i.e., 6 We use the Berkeley Parser Analyser to analyze types of parse error (Kummerfeld et al., 2012). Our turn-based model performs worse overall on disfluent turns than on fluent turns, which was also true of Tran et al. (2018)'s SU-based model. Prosody also leads to a greater gain in F1 for disfluent turns than for fluent turns. These differences in performance are shown in Table 4. The lower performance on disfluent sentences may be at least partially attributable to length differences: the median length of turns with disfluencies is 28 tokens, compared to 3 tokens for fluent turns, where we define a disfluent turn as any turn containing the constituent tag EDITED . As discussed in Section 5, longer input generally leads to more parser errors, meaning that disfluent sentences are more likely to cause parser errors. However, there are other reasons disfluencies are difficult for the turn-based model, as discussed in the following section. One effect of disfluencies is that the text-only model tends to confuse certain kinds of disfluencies for SU boundaries, as illustrated in Figure 1. Table 5 shows that the text+prosody model largely avoids this confusion, and indeed can do so almost as well using only pitch or intensity features. However, models using only pause or duration features are not good at distinguishing disfluencies from SU boundaries and predict boundaries too often. These results largely concur with previous work describing the similarities and differences between prosodic features of disfluencies and SU boundaries (Shriberg, 2001; Wagner and Watson, 2010). (and do not) accord with expectations. The disfluencies that are relevant to this discussion include repetitions and restarts. Examples of these from SWBD-NXT are shown here, with bracketing added for clarity: (3) Spurious repetition: it [may] may be at this point Restart: [but it's] but I think it's relatively unimportant In these examples, the text in square brackets is called the reparandum , which is immediately followed by the interruption point . Disfluencies in SWBD-NXT are marked in the constituency parse annotation, where the reparandum is marked as a constituent with the label EDITED . The interruption point is the right edge of this constituent. Our model may be able to learn such temporal patterns, but even just looking at static pitch features re-R ETRACTED This paper was retracted. Our analysis draws on the work of Shriberg (2001), who described the prosodic features of the interruption point and the reparandum based on an analysis of three English conversational and task-based dialogue corpora  the Switchboard Corpus (which we use a subset of), ATIS (Hirschman, 1992), and AMEX (Kowtko and Price, 1989). Pauses. Although pauses may be the most intuitive potential cue to SU boundaries, previous work suggests that long pauses also characterize interruption points (Wagner and Watson, 2010; Shriberg, 2001). Indeed, our analysis shows that longer pauses ( > 0 . 05 s ) are over-represented in both locations. If pause types were distributed uniformly, 16 percent of both SU boundaries and interruption points would have a longer pause. Instead, we find that 33 percent of SUs boundaries and 37 percent of interruption points have such pauses. This explains why the pause-only model tends to confuse SU boundaries and interruption points. Duration. Shriberg (2001) found that both interruptions and SU boundaries are associated with lengthening of the immediately preceding syllable. Lengthening before the interruption point may occur even if there are no other prosodic cues to the disfluency, and can be far greater than at SU boundaries (Shriberg, 2001, 161). This type of lengthening is captured by our first duration feature, which measures the token duration normalized by the mean duration for its word type. Like Shriberg (2001), we find that words preceding SU boundaries are lengthened on average (normalized duration: 1.18), and those preceding interruption points even more so (normalized duration: 1.41). In prin-ciple, this extra lengthening could help the duration-only model distinguish SU boundaries from interruptions, but in practice the model is nearly as bad at distinguishing them as the text-only model. The second duration feature is the token length normalized by the maximum length of any token in the input, to normalize for speaking rate. Initially, this feature looks helpful: SU-final words have mean value of 0.86, while words directly before the interruption point have a mean of 0.50. However, the feature mainly captures the number of phones in a word, since words with fewer phones  including English function words  tend to have shorter normalized duration. It turns out that function words occur more often before interruption points than before SU boundaries: using NLTK's stopwords as a heuristic for function words, only 21.9 percent of development set SUs end in a function word, while the word before an interrutption point is a function word 51.6 percent of the time (Bird and Klein, 2009). Since the second duration feature captures a lexical distinction that is already signalled in the text, it cannot help the duration-only model outperform the text-only model. Pitch. Based on previous work, our finding that pitch features are useful is not a surprise: the pitch contour before an interruption point is generally flat or slowly falling (Shriberg, 2001, 161), while SU boundaries are characterized by a boundary tone , generally corresponding to a fall or rise. veals differences between boundaries and interruptions for two of the three features. In particular, the mean warped NCCF value for pre-interruption point words is significantly higher than the value for SU-final words ( p < 0 . 001 ), though somewhat lower than the overall average value across the development set. Meanwhile, the log-pitch with POV-weighted mean subtraction is significantly lower at interruption points than at SU boundaries ( p < 0 . 01 ). These differences allow the pitch-only model to distinguish SU boundaries and interruption points much better than the pauseor duration-only models can (see Table 5). Of these two pitch features, log-pitch is a more direct indicator of fundamental frequency (F0), which suggests that average perceived pitch is likely lower before disfluencies than before SU boundaries. There could be several reasons for this difference. For example, it could be that the flat or slowly falling tone of disfluencies that Shriberg (2001) describes has a lower average value than SU boundaries which can have either a fall or a rise (e.g., for certain kinds of questions). However, examining pitch features across the whole corpus obscures more subtle distinctions such as different types of pitch contours. Intensity. We find that intensity features alone are enough to distinguish SU boundaries from interruption points, which is interesting because intensity has not been previously identified as an important cue: Shriberg (2001) doesn't note any particularly distinctive intensity features of the reparandum or interruption point, and work by Kim et al. (2006) on the Switchboard Corpus suggests that SU boundaries are correlated to lower intensity in some speakers, but that this isn't consistent across speakers. The three intensity features correspond to overall energy, energy in the lower half of frequencies, and energy in the higher frequencies. SU-final words have a significantly higher mean value for lower-frequency intensity than all other words ( p < 0 . 001 ), while words before the interruption point do not. This systematic difference in one intensity feature seems to be part of how intensity features allow the model to consistently tell SU boundaries apart from disfluencies. RETRACTED This paper was retracted. Overall performance. Given our claim that the main issue facing the text-only turn-based parser is distinguishing disfluencies from SU boundaries, it is not surprising that the two features that do best at this, pitch and intensity, also yield the highest overall performance. Results are shown in Table 6. 7 Conclusion Our experiments show that parsing English speech transcriptions without gold SU boundaries is difficult for our parser: Its F1 score drops by about 4 percentage points compared to a model with gold SU boundaries. Incorrect SU segmentation causes a large part of this damage, though other errors in tree construction also play a role. We show that we can undo this damage by giving our parser prosodic information. Importantly, prosody helps by allowing the parser to distinguish disfluencies from SU boundaries. These results argue for giving prosodic information to parsers in deployed applications, where no SU boundary annotations are available, including dialog agents. Furthermore, our experiments show that even limited prosodic features help a great deal: for our English data, pitch information alone is not significantly worse than pitch, intensity, pause, and word duration information combined. This means that incorporating the right kind of prosodic information can potentially lead to significant gains. We are very grateful to Trang Tran for help answering questions related to her code and to Mari Ostendorf for conversations that helped inspire this paper. We would like to thank Korin Richmond, the ACL reviewers, and members of the AGORA research group at the University of Edinburgh for their feedback. This work was supported by funding from Huawei and the project SEMANTAX, which received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 742137). 988 References Tanvirul Alam, Akib Khan, and Firoj Alam. Edward Loper Bird, Steven and Ewan Klein. 2009. Natural Language Processing with Python . O'Reilly Media Inc. Eugene Charniak and Mark Johnson. 2001. Edit detection and parsing for transcribed speech. In Second Meeting of the North American Chapter of the Association for Computational Linguistics . Anne Cutler, Delphine Dahan, and Wilma van Donse-laar. 1997. Prosody in the comprehension of spoken language: A literature review. Language and Speech , 40(2):141201. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , pages 41714186, Minneapolis, Minnesota. Association for Computational Linguistics. Marcello Federico, Sebastian Stuker, Luisa Bentivogli, Michael Paul, Mauro Cettolo, Teresa Herrmann, Jan Niehues, and Giovanni Moretti. 2012. The iwslt 2011 evaluation campaign on automatic talk translation. In International Conference on Language Resources and Evaluation (LREC) , pages 35433550. 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