| { | |
| "paper_id": "2020", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T13:27:12.116276Z" | |
| }, | |
| "title": "Coreference for Discourse Parsing: A Neural Approach", | |
| "authors": [ | |
| { | |
| "first": "Grigorii", | |
| "middle": [], | |
| "last": "Guz", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "University of British Columbia Vancouver", | |
| "location": { | |
| "postCode": "V6T 1Z4", | |
| "region": "BC", | |
| "country": "Canada" | |
| } | |
| }, | |
| "email": "[email protected]" | |
| }, | |
| { | |
| "first": "Giuseppe", | |
| "middle": [], | |
| "last": "Carenini", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "University of British Columbia Vancouver", | |
| "location": { | |
| "postCode": "V6T 1Z4", | |
| "region": "BC", | |
| "country": "Canada" | |
| } | |
| }, | |
| "email": "[email protected]" | |
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| "abstract": "We present preliminary results on investigating the benefits of coreference resolution features for neural RST discourse parsing by considering different levels of coupling of the discourse parser with the coreference resolver. In particular, starting with a strong baseline neural parser unaware of any coreference information, we compare a parser which utilizes only the output of a neural coreference resolver, with a more sophisticated model, where discourse parsing and coreference resolution are jointly learned in a neural multitask fashion. Results indicate that these initial attempts to incorporate coreference information do not boost the performance of discourse parsing in a statistically significant way.", | |
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| "abstract": [ | |
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| "text": "We present preliminary results on investigating the benefits of coreference resolution features for neural RST discourse parsing by considering different levels of coupling of the discourse parser with the coreference resolver. In particular, starting with a strong baseline neural parser unaware of any coreference information, we compare a parser which utilizes only the output of a neural coreference resolver, with a more sophisticated model, where discourse parsing and coreference resolution are jointly learned in a neural multitask fashion. Results indicate that these initial attempts to incorporate coreference information do not boost the performance of discourse parsing in a statistically significant way.", | |
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| "section": "Abstract", | |
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| "text": "Discourse parsing is a very useful Natural Language Processing (NLP) task involving predicting and analyzing discourse structures, which represent the coherence properties and relations among constituents of multi-sentential documents. In this work, we investigate discourse parsing in the context of Rhetorical Structure Theory (RST) Mann and Thompson (1988) , which encodes documents into complete constituency discourse trees. An RST tree is defined on the sequence of a document's EDUs (Elementary Discourse Units), which are clause-like sentences or sentence fragments (propositions), acting as the leaves of the tree. Adjacent EDUs and constituents are hierarchically aggregated to form (possibly non-binary) constituents, with internal nodes containing (1) a nuclearity label, defining the importance of that subtree (rooted at the internal node) in the local context and (2) a relation label, defining the type of semantic connection between the two subtrees (e.g., Elaboration, Background). (Asher and Lascarides, 2003) of a discourse being ill-formed due to the invalid anaphoric link. Previous research has shown that the use of RSTstyle discourse parsing as a system component can enhance important tasks, such as sentiment analysis, summarization and text categorization (Bhatia et al., 2015; Nejat et al., 2017; Hogenboom et al., 2015; Gerani et al., 2014; Ji and Smith, 2017) . And more recently, it has been found that RST discourse structures can complement learned contextual embeddings (e.g., BERT (Devlin et al., 2018) ), in tasks where linguistic information on complete documents is critical, such as argumentation analysis (Chakrabarty et al., 2019) .", | |
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| "text": "(1988)", | |
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| "text": "(Asher and Lascarides, 2003)", | |
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| "text": "Nejat et al., 2017;", | |
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| "text": "Hogenboom et al., 2015;", | |
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| "text": "Gerani et al., 2014;", | |
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| "start": 1371, | |
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| "text": "Ji and Smith, 2017)", | |
| "ref_id": "BIBREF16" | |
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| "start": 1517, | |
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| "text": "(Devlin et al., 2018)", | |
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| "text": "(Chakrabarty et al., 2019)", | |
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| "section": "Introduction and Task Description", | |
| "sec_num": "1" | |
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| "text": "In this work, we present preliminary results of investigating the benefits of coreference resolution features for RST discourse parsing. From the theoretical perspective, it has long been established (Asher and Lascarides, 2003) that discourse structure can impose constraints on mention antecedent distributions, with these constraints being derived from the role of each discourse unit (sen-tence or EDU) with respect to the global discourse. The Veins theory (Cristea et al., 1998) is the most known formalization of anaphoric constraints with respect to RST tree structures, involving assigning to each EDU a subset of preceding EDUs defined by the nuclearity attributes of the EDU's parent nodes in the document's discourse tree (see Appendix A for the exact definition). These constrains act as a domain of referential accessibility where the antecedents must reside, for otherwise the discourse would be considered incoherent. As an example of this phenomenon, consider the discourse structure in Figure 1 . In principle, a reader could apply commonsense knowledge to resolve the pronoun it in the last sentence to salmon in the third sentence, any proficient English speaker would call such a discourse ill-formed and incoherent, due to the fact that it breaks the discourse-imposed antecedent scope. In general, anaphora can only be resolved with respect to the most salient (sentence 1 in Figure 1 ) units of the preceding discourse (Asher and Lascarides, 2003) . For our purposes, this means that having access to a document's coreference structure might be beneficial to the task of predicting the discourse structure, since the coreference structure can constrain the discourse parser's solution space. However, as shown in a corpus study by Zeldes (2017) , the antecedent boundaries defined by Veins Theory are often too restrictive, suggesting that while discourse structures can be useful for predicting coreference structures and vice versa, these mutual constrains must be defined softly, at least in the context of RST theory.", | |
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| "text": "(Cristea et al., 1998)", | |
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| "text": "Figure 1", | |
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| "text": "Figure 1", | |
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| "section": "Introduction and Task Description", | |
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| "text": "To explore these ideas computationally with respect to modern neural models, we investigate the utility of automatically extracted coreference features and discourse-coreference shared representations in the context and for the benefit of neural RST discourse parsing. Our strong baseline SpanBERT-NoCoref utilizes SpanBERT (Joshi et al., 2020) as in the current SOTA coreference resolver, without utilizing any direct coreference information. Next, our SpanBERT-CorefFeats considers the output of coreference resolver as per Dai and Huang (2019) , letting us test the benefit of predicted and so possibly noisy coreference features. Finally, our more sophisticated SpanBERT-Multitask model learns discourse parsing together with coreference resolution in the neural multitask learning fashion, sharing the SpanBERT contextual word encoder for both models.", | |
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| "text": "Dai and Huang (2019) have already explored the benefit of using coreference information for neural PDTB implicit discourse relation classification, in a way similar to our SpanBERT-CorefFeats model. In our study, we also explore the use of shared encoder architecture for both tasks to detect the additional possible synergy.", | |
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| "section": "Related Work", | |
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| "text": "Modelwise, the most common approach to infer discourse trees is the linear bottom-up shiftreduce method, adopted from syntactic parsing. Wang et al. (2017) uses hand-crafted features and the shift-reduce method predicted by two separate Support-Vector-Machines (SVMs) for structureand nuclearity-prediction and relation-estimation. The neural model by Yu et al. (2018) uses a similar topology, but instead relies entirely on LSTMs for automatic feature extraction and on a single multilayer-perceptron (MLP) for classifying all possible actions. Top-down approaches to discourse parsing are also quite promising, with recent work of Kobayashi et al. (2020) applying ELMO (Peters et al., 2018) for computing span representations and achieving the new absolute SOTA performance, reporting however the scores of an ensemble of five independent runs of their proposed model instead of single-model results. In this work we follow the shift-reduce strategy and apply SpanBERT-Base (Joshi et al., 2020; Wolf et al., 2020 ), which we introduce below, for encoding the document contents.", | |
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| "text": "The field of coreference resolution has recently been dominated by deep learning models. The current SOTA model by Joshi et al. (2020) is built upon the neural coreference resolver of by incorporating SpanBERT language model, which modifies the commonly used BERT (Devlin et al., 2019) architecture with a novel span masking pretraining objective. In our work, we reimplemented their coreference resolver in PyTorch (Paszke et al., 2019) . Our code for both models is available 1 .", | |
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| "text": "All our proposed parsers share the same basic shiftreduce architecture, consisting of a Queue, which is initially filled with documents EDUs in order from first to last one, and a Stack, which is initially empty, as well as the following actions on them:", | |
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| "section": "Shift-Reduce Architecture", | |
| "sec_num": "3" | |
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| { | |
| "text": "w i ... w i+1 w i+2 ... w j w j+1 ... w i+1 ... w i ... w j w j+1 ... s i ... s i+1 s i+2 ... s j s j+1 ... s i ... s i+1 s i+2 ... s j s j+1 ... SpanBERT v S2 v S1 w i+2 v S2 v S1 w ' i+1 w ' i ... w ' j w ' j+1 w ' i+2 ... ... ... ... S 2 S 1", | |
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| "section": "Shift-Reduce Architecture", | |
| "sec_num": "3" | |
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| "text": "The Shift delays aggregations of sub-trees at the beginning of the document by popping the top EDU Q 1 on the queue and pushing it onto the stack.", | |
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| "section": "Shift-Reduce Architecture", | |
| "sec_num": "3" | |
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| "text": "The Reduce-X aggregates the top subtrees (S 1 , S 2 ) on the stack into a single subtree (S 1\u22122 ). Each reduce action further defines a nuclearity assignment X N \u2208 {NN, NS, SN} to the nodes covered by S 1\u22122 and a relation X R \u2208 {Elaboration, Contrast, ...} holding between them.", | |
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| "section": "Shift-Reduce Architecture", | |
| "sec_num": "3" | |
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| "text": "Similarly to (Wang et al., 2017; Yu et al., 2018) , all models under consideration utilize the information from top two elements S 1 , S 2 of the stack, and top element Q 1 of the queue. In addition to word-/word+coference-based representations", | |
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| "section": "Action Classifier Parametrization", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "v S 2 , v S 1 , v Q 1", | |
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| "section": "Action Classifier Parametrization", | |
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| "text": "for these nodes, computed differently by each model as described below, we extract textual organization features of Wang et al. (2017) . In particular, for each pair S 2 \u2212 S 1 and S 1 \u2212 Q 1 , we extract indicator features representing whether the pair is within the same sentence or paragraph; for each of S 2 , S 1 and Q 1 we compute whether each of them are at the start/end of a sentence/paragraph/document.", | |
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| "start": 116, | |
| "end": 134, | |
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| "section": "Action Classifier Parametrization", | |
| "sec_num": "3.1" | |
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| "text": "In accord with Wang et al. (2017) , the parsing action at each timestep is chosen by two trainable classifiers, being multi-layer perceptrons (MLPs) in our system, where each classifier takes in the concatenation of v S 2 , v S 1 , v Q 1 , together with the dense embeddings for the aforementioned organizational features. The first classifer predicts the action and nuclearity assignment among y Act,N uc \u2208 {Shif t, Reduce N N , Reduce N S , Reduce SN }, and in case the Reduce action is chosen, the second classifier predicts the discourse relation among 18 coarse-grained RST relation classes y Rel \u2208 {Attribution, Elaboration, ...}.", | |
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| "section": "Action Classifier Parametrization", | |
| "sec_num": "3.1" | |
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| "text": "Both classifiers are trained using the Cross-Entropy loss, computed for each Stack-Queue parsing step. At test time, we apply the greedy decoding strategy to predict the discourse structure.", | |
| "cite_spans": [], | |
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| "section": "Action Classifier Training and Inference", | |
| "sec_num": "3.2" | |
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| { | |
| "text": "We now describe the three proposed discourse parsing models which differ in the levels of coupling with the coreference model. See Figure 2 for the visual comparison.", | |
| "cite_spans": [], | |
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| "start": 131, | |
| "end": 139, | |
| "text": "Figure 2", | |
| "ref_id": "FIGREF1" | |
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| "section": "Proposed Models", | |
| "sec_num": "4" | |
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| "text": "SpanBERT-NoCoref: in addition to the organizational features, our baseline system utilizes only the output SpanBERT-contextualized word embeddings. To predict each Stack-Queue action, a full document is passed through SpanBERT in a non-overlapping sliding window fashion, as per Joshi et al. (2020) , so that the context of full document can be considered for each parsing action to account for possible context-sensitivity of discourse structures (Dai and Huang, 2018) . The node representation v Q 1 for the first Queue element is computed as the mean of the first and the last word embedding of the EDU that this Queue element represents. v S 1 and v S 2 are computed as the means of the first and the last word embeddings of the nuclear EDU of S 1 and S 2 , as each non-leaf node in an RST structure encodes a relation between nuclear EDUs of its children (Morey et al., 2018) .", | |
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| "text": "SpanBERT-CorefFeats: with this architecture variant, we attempt to assess the benefit of coreference features generated by the coreference resolver for RST parsing. Given a document with n words, the coreference features will be used to update the initial (not contextualized) SpanBERT word embeddings w 1:n , which will later be passed to SpanBERT.", | |
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| "text": "Specifically, for a given document we apply the pre-trained coreference parser of Joshi et al. (2020) to extract the document's coreference clusters C 1 , C 2 , ..., each of which are equivalence classes representing different mentions of the same entity. Afterwards, we compute the vector representation c i for each cluster C i by performing attention-based averaging over word-vectors corresponding to mentions in that cluster:", | |
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| { | |
| "text": "c i = k\u2208C i a k w k", | |
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| "text": "where w k \u2208 R d is the initial SpanBERT word embedding for word k and a k \u2208 [0, 1] are attention scores. These cluster representations are then used for updating the document's word representations using the gating mechanism : for each word w k \u2208 D,", | |
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| "text": "f k = \u03c3(W [c i ; w k ]) w k = f k \u2022 w k + (1 \u2212 f k ) \u2022 c i if w k \u2208 C i w k otherwise", | |
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| "text": "Finally, the embeddings w k are passed to Span-BERT for contextualization, and the node rep-", | |
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| { | |
| "text": "resentations v S 1 , v S 2 , v Q 1 are computed as in SpanBERT-NoCoref.", | |
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| "text": "SpanBERT-MultiTask: learns discourse parsing and coreference resolution in a multitask learning regime, weight-sharing the SpanBERT encoder module. The coreference resolver training step proceeds in the same fashion as in (Joshi et al., 2020) . For updating the discourse parsing model, we use the pre-computed coreference clusters C i obtained from the pretrained coreference model, as running it at every training step was prohibitively time-consuming. Using the contextualized Span-BERT word embeddings s 1:n for all words in the document, we check these coreference clusters for overlaps: considering a pair of spans S 1 , S 2 , if a cluster C i has entity mentions in the spans of both stack elements S 1 and S 2 , so that if there are mentions m j , m k \u2208 C i such that m j \u2208 S 1 and m k \u2208 S 2 , we update the span representation v S 1 (computed as in SpanBERT-NoCoref) with the attention weighted sum of mentions m k \u2208 C i \u2229S 2 by applying the gating mechanism as in SpanBERT-CorefFeats, so that the span representation for S 1 can incorporate more relevant context from S 2 . The representation for v S 2 is computed similarly using mentions m k \u2208 C i \u2229 S 1 , and the analogous computation is performed for S 1 \u2212 Q 1 pair.", | |
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| "text": "For learning both tasks at the same time, we utilize the approach similar to (Sanh et al., 2018) , where gradient updates are performed separately for each task and the probability of sampling a task is proportional to the relative size of each task's dataset. The initial shared SpanBERT encoder weights are set from the pretrained coreference resolver checkpoint. ", | |
| "cite_spans": [ | |
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| "text": "(Sanh et al., 2018)", | |
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| "text": "All models were trained on the RST-DT (Carlson et al., 2002) and evaluated with RST-Parseval procedure (Marcu, 2000) , with the coreference component of SpanBERT-MultiTask being trained on full OntoNotes 5.0 corpus (Weischedel et al., 2013) . The details of the training procedure such as hyperparameter assignment are outlined in the Appendix B. The test results are presented in Table 1 and are the average and standard deviation single-model scores of five independent runs. Firstly, we observe that our models strongly outperform all previous approaches, indicating huge benefit of pretrained language models for RST discourse parsing, with results approaching human performance. Then, with respect to coreference features, we notice that the models utilizing coreference information are statistically equivalent in performance to the SpanBERT-NoCoref baseline, while displaying higher variance of the test scores for Nuclearity and Relation prediction. This suggests four plausible (and not mutually exclusive) explanations: (1) the coreference information relevant to discourse parsing is already captured by SpanBERT, (2) or that coreference information is not a strong signal for discourse structure (Zeldes, 2017) , or that (3) the coreference information extracted automatically is too noisy, or that (4) our specific ways of combining coreference with discourse parsing are not adequate and more work is needed to develop better solutions. It should also be noted that we only experimented on a single discourse parsing dataset, so the conclusions or generalizations should be considered preliminary.", | |
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| "sec_num": "5" | |
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| "text": "In an attempt to shed some light on the results, we compare the predicted and gold subtree from one of the documents in our development set on Figure 3 . The trees were analyzed using the RST tree visualization tool by Huber (2019) . According to Veins theory, the pronoun [neither] in EDU 8 is a mention that should have access to its mentions ([Finnair and SAS] or [they]) in preceding EDUs. However, accoding to the discourse structure predicted by SpanBERT-NoCoref, the vein for node (8) does not contain the EDUs (6) and 7(and in fact any of its preceding EDUs), so that [neither] cannot be linked to any of its preceding mentions. On the other hand, according to the gold discourse structure, EDU 8 has EDU 7 on its vein, meaning that this anaphora can be resolved. This means that if one had access to gold coreference structure and applied Veins Theory strictly, the substructure produced by SpanBERT-NoCoref would not be permitted.", | |
| "cite_spans": [ | |
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| "end": 231, | |
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| "start": 273, | |
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| "text": "[neither]", | |
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| "text": "Figure 3", | |
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| } | |
| ], | |
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| "section": "Experimental Settings and Results", | |
| "sec_num": "5" | |
| }, | |
| { | |
| "text": "We empirically compare different levels of coupling between a shift-reduce neural discourse parser and a neural coreference resolver. Remarkably, our baseline delivers SOTA performance on RST-DT, but does not seem to benefit from coreference features. For future work, we plan to experiment with (1) alternative discourse parsing architectures and approaches for neural multitasking, along with more powerful coreference models (2) alternative ways of augmenting a neural discourse parser with coreference information and other tasks like summarization (3) improving the coreference resolution performance by leveraging information provided by a discourse parser.", | |
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| "section": "Conclusions and Future Work", | |
| "sec_num": "6" | |
| }, | |
| { | |
| "text": "http://www.cs.ubc.ca/ cs-research/lci/research-groups/ natural-language-processing/index.html", | |
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| { | |
| "text": "The following definitions are from Cristea et al. (1998) . For each node in an RST tree, its head is defined as follows:1. The head of the terminal (leaf) node is itself.2. The head of a non-terminal node is the concatenation of the heads of its nuclear children.Next, we define the vein expression of each node recursively top-down. When the node is a leaf, the preceding nodes on its vein correspond to its domain of referential accessibility.1. The vein expression of the root is its head.2. For each nuclear node, its vein expression is the union of its head with:\u2022 its parent's vein, if this node has no left siblings that are satellites. \u2022 its parent's vein and its left sibling's vein, if this sibling is a satellite.3. For every satellite node, its vein expression is the union of its head with:\u2022 its parent's vein, if this node is a left child. \u2022 its parent's vein with heads of prior (up in the tree) satellite nodes removed.", | |
| "cite_spans": [ | |
| { | |
| "start": 35, | |
| "end": 56, | |
| "text": "Cristea et al. (1998)", | |
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| } | |
| ], | |
| "ref_spans": [], | |
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| "section": "A Veins Theory Definitions", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "As RST-DT does not specify a standard trainingvalidation split, we select 10% of the training documents for the validation set, stratifying the split by the number of EDUs in each document. Similarly to Joshi et al. 2020, we train all of our models with AdamW (Loshchilov and Hutter, 2019) optimizer with learning rate of 1e \u22125 for SpanBERT and 2e \u22124 for model-specific components, with the batch size of 5 and linear decay for 20 epochs. All of our MLPs consist of 2 linear layers, with a GeLU (Hendrycks and Gimpel, 2016) nonlinearity and a Dropout layer with a value of 0.3 between them. Each organizational feature of Wang et al. (2017) is represented using a learnable 10-dimentional embedding, or a vector of zeros if the feature is missing (for example, the feature specifying if the 2-top elements of the stack are in the same sentence when the stack contains only one element). With regards to multitask regime, the probability of discourse parsing task being sampled over coreference resolution was \u2248 0.72 (each Stack-Queue state was treated as a datapoint), but due to highly demanding computational requirements of the coreference resolver and time constrains, this probability was increased to 0.9. Nonetheless, the results for the correct task proportions will be provided through other sources.", | |
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| "FIGREF0": { | |
| "type_str": "figure", | |
| "text": "An example", | |
| "num": null, | |
| "uris": null | |
| }, | |
| "FIGREF1": { | |
| "type_str": "figure", | |
| "text": "Overview of our models. For spans S 2 are S 1 , the w i:i+2 and w j,j+1 respectively are the nuclear EDUs. (Left) All components of SpanBERT-NoCoref. (Middle) SpanBERT-CorefFeats modifies the initial SpanBERT embeddings according to predicted coreference clusters (in red). (Right) SpanBERT-MultiTask updates the final span representations with embeddings of mentions of shared entities.", | |
| "num": null, | |
| "uris": null | |
| }, | |
| "FIGREF2": { | |
| "type_str": "figure", | |
| "text": "A subtree from SpanBERT-NoCoref prediction for wsj 0631 (left) and gold-standard (right", | |
| "num": null, | |
| "uris": null | |
| }, | |
| "TABREF0": { | |
| "html": null, | |
| "text": "The leaf EDUs are as follows: [Max had a great evening last night.] 1 [He had a great meal.] 2 [He ate salmon.] 3 [He devoured lots of cheese.] 4 [He then won a dancing competition.] 5 [It was a beautiful pink] 6", | |
| "content": "<table/>", | |
| "type_str": "table", | |
| "num": null | |
| }, | |
| "TABREF1": { | |
| "html": null, | |
| "text": "Parseval micro precision for structure, nuclearity and relation prediction on RST-DT corpus. Scores for previous approaches are from eitherMorey et al. (2017) or the original papers.", | |
| "content": "<table><tr><td>Model</td><td colspan=\"2\">Structure Nuclearity</td><td>Relation</td></tr><tr><td>HILDA(2010)</td><td>82.6</td><td>66.6</td><td>54.6</td></tr><tr><td>DPLP(2014)</td><td>82.0</td><td>68.2</td><td>57.8</td></tr><tr><td>CODRA(2015)</td><td>82.6</td><td>68.3</td><td>55.8</td></tr><tr><td>Two-Stage(2017)</td><td>86.0</td><td>72.4</td><td>59.7</td></tr><tr><td>Transition-Syntax(2018)</td><td>85.5</td><td>73.1</td><td>60.2</td></tr><tr><td>D2P2S2E (Ensemble)(2020)</td><td>87.0</td><td>74.6</td><td>60.0</td></tr><tr><td>SpanBERT-NoCoref</td><td colspan=\"3\">87.8 \u00b1 0.2 75.8 \u00b1 0.2 63.4 \u00b1 0.3</td></tr><tr><td>SpanBERT-CorefFeats</td><td colspan=\"3\">88.1 \u00b1 0.3 76.1 \u00b1 0.6 63.6 \u00b1 0.3</td></tr><tr><td>SpanBERT-MultiTask</td><td colspan=\"3\">87.9 \u00b1 0.2 75.9 \u00b1 0.6 63.3 \u00b1 0.7</td></tr><tr><td>Human (2017)</td><td>88.3</td><td>77.3</td><td>65.4</td></tr><tr><td>Table 1: RST-</td><td/><td/><td/></tr></table>", | |
| "type_str": "table", | |
| "num": null | |
| } | |
| } | |
| } | |
| } |