Unifying Discourse Resources with Dependency Framework (2021)

Abstract: For text-level discourse analysis there are various discourse schemes but relatively few labeleddata because discourse research is still immature and it is labor-intensive to annotate the innerlogic of a text. In this paper we attempt to unify multiple Chinese discourse corpora under different annotation schemes with discourse dependency framework by designing semi-automatic methods to convert them into dependency structures. We also implement several benchmark dependency parsers and research on how they can leverage the unified data to improve performance.1

## introduction discourse parsing aims to construct the logical structure of a text and label relations between discourse units, as shown in fig. 1 and fig. 2 . various discourse corpora have been developed to promote the discourse parsing technique. there exist multiple discourse schemes such as rhetorical structure theory (rst) @xcite and pdtb @xcite , which act as guidelines of various discourse corpora. at the same time, text-level discourse annotation is complicated and laborious, so the scale of a single discourse corpora is often much smaller compared with other nlp tasks. one way to conquer data sparsity is to use different discourse corpora through multi-task learning. one way to conquer the data-sparsity problem is to multi-task learning multiple corpora simultaneously. prior efforts on that mainly focus on discourse relation classification between two text spans, without considering the whole discourse structure @xcite , since one principle of multitask learning is that the tasks should be closely related, but the discourse structures of different corpora vary a lot, e.g. shallow predicate-arguments structure (e.g. the relations in fig. 1 ) and deep tree structure (e.g. the cdtb tree in fig. 2 ). in this work, we explore another possible way to simultaneously leverage different corpora: we unify the existing discourse corpora under one same framework to form a much larger dataset. our choice for this unified framework is dependency discourse structure (dds), where elementary discourse units (edus) are directly related to each other without consideration of intermediate text spans. because as @xcite have argued, dds is a very general discourse representation framework, and they adopt a dependency perspective to evaluate the english discourse corpora rst-dt @xcite and the related parsing techniques. our work explore the feasibility of unifying discourse resources under dependency framework with consideration of the following two questions: (i) how to convert other discourse structures into dds? (ii) how to make the best use of the unified data to improve discourse parsing techniques? oriented by the questions above, we unify three chinese discourse corpora under dependency framework: hit-cdtb @xcite , su-cdtb @xcite and sci-cdtb @xcite . hit-cdtb adopts the predicate-argument structure similar to pdtb, with a connective as predicate and two text spans as arguments. following rhetorical structure theory (rst), su-cdtb uses a hierarchical tree to represent the inner structure of each text, with edus as its leaves and connectives as intermediate 1 the data is available at https://github.com/pku-tangent/unifieddep . ## background in this section we mainly introduce the three chinese discourse corpora: hit-cdtb, su-cdtb and sci-cdtb, which we use in this work. hit-cdtb borrowing the discourse scheme of pdtb, hit-cdtb adopts one-predicate two-arguments structure, where a connective serves as the predicate and two text spans as two arguments, as shown in fig. 1 . the connective can be either explicitly identified if it already exists in the original text, or implicitly added by annotators, while the arguments can be phrases, clauses, sentences, or sentence groups. each connective corresponds to a relation type. in total, there are 4 coarse-grained types (i.e., temporal causal, comparative and extension) and 22 fine-grained types (e.g., temporal is further divided as synchronous and asynchronous). the documents cover multiple domains, such as news, editorials and popular science articles. when labeling each document, the discourse relations are labeled locally by only considering adjacent text spans, so a complete logical structure of the text may not be obtained. su-cdtb similar to rst, su-cdtb represents the inner structure of a text with a hierarchical tree with edus as leaves, and uses connectives as intermediate nodes to indicate rhetorical relations. the whole text is first divided into several text spans which are recursively divided until getting edus. in su-cdtb, edus are segmented according to the punctuation marks. the nucleus-satellite relation structure in rst is retained through arrows in the trees. connectives in su-cdtb are also given some relation attributes. 500 news documents from the chinese treebank @xcite are annotated. a discourse tree is constructed for each paragraph rather than the entire document, with an average of 4.5 edus per tree, which are relatively shallow. besides, the top-down constructed tree cannot cover some particular discourse structures, such as non-adjacent relations. so parsers trained only with this corpus are not very likely to analyze more complex discourse logic. sci-cdtb sci-cdtb is a small chinese discourse dependency corpus, where edus are directly connected with discourse relations without intermediate levels. its definition of edu refers to rst-dt, with some modifications based on the linguistic characteristics of chinese. the head of each edu and the relations between them are annotated. sci-cdtb is a small-scale corpus, only composed of 108 annotated c c l 2 0 2 1 conversion result： u 3 u 4 u 5 u 6 u 2 u 1 causality temporal causality adversative parallel inner-sentence relation： s 1 ：[一渔船昨晚出海时]u 1 [冷动机突然发生故障。]u 2 when a fishing boat was sailing out at sea last night, its cold motor broke down. s 2 ：[ 结果造成机长和３名渔工眼睛受伤，]u 3 [还 and 合并有呕吐现象。]u 4 as a result, the captain and three fishermen suffered eye injuries, and kept vomiting. s 3 ：[不过当时渔船已经失去了动力，]u 5 (所以 causality ) [受伤的人员只能在渔船上等待救援。]u 6 but by then the boat had lost power, (so) the injured can only wait on the boat to be rescued. inter-sentence relation: s 1 结果 causality s 2 scientific abstracts, so it is hard to support the training of a competitive discourse parser. it is the only chinese dds corpus to the best of our knowledge. ## unifying discourse corpora in this section, we introduce how to convert the three discourse corpora into one unified framework, which mainly involves two aspects, i.e., edu segmentation and dependency tree construction. here, we adopt the edu segmentation guideline of sci-cdtb, which is similar to rst-dt @xcite . basic discourse units of su-cdtb and hit-cdtb are divided mainly based on punctuation marks, which is inconsistent with our edu definition, so we manually re-segment their edus to ensure the same segmentation rules. as for dependency tree construction, we should ensure correctness of both the tree structure and the relations between them. in a dependency tree, each relation connects a head edu to a dependent edu. each edu should have one and only one head. as the three corpora adopt different relation sets (22 relation types in hit-cdtb dep , 18 in su-cdtb dep 2 and 26 in sci-cdtb), during conversion we keep the relations unchanged for each corpus, and then map them into 17 predefined relation types, which are basically the same as the ones of su-cdtb, except that relation example illustration is merged into explanation. it requires to be further investigated whether there is a more appropriate mapping, and there exists the possibility that these relation sets have inherent incompatibility. experiments are conducted both before and after relation mapping. ## dependency discourse parsing baselines following the work of @xcite , we implement four chinese discourse dependency parsers: metrics as for metrics, we use uas and las to measure the dependency tree labeling accuracy without and with relation labels respectively. las o and las u denote using the original relation set of each corpus results graph-based method is less effective than the others, but it could probably be improved by using other training methods like mira. among the transition-based methods, bert-based performs the best on su-cdtb dep and sci-cdtb dep with respect to las, but is 2.4% lower than two-stage on hit-cdtb dep , probably because hit-cdtb dep is a multi-domain corpus, and feature-based methods are more robust to changes in different domains. comparing the three corpora, parsing results on sci-cdtb are the worst because sci-cdtb is too small for supervised learning. as we expect, su-cdtb performs the best since it has relatively shallow tree structure and its labeling is highly consistent. with original relations mapped to the unified relation set, las u results of all the methods on the three corpora have been improved compared with las o , proving that the unified relation set we use is acceptable. two-stage transition-based method performs better than the vanilla one with respect to las due to the addition of tree structural features in relation type prediction. two-stage uni and bert-based uni use unified dep as training data, and are tested on each corpus respectively. from for las u , both methods increase on hit-cdtb dep , but drop on su-cdtb dep . as hit-cdtb dep covers multiple domains and has an average of 28.6 edus per text, its annotation is more difficult and the dds-conversion process is also more complicated. in comparison, with an average of 4.5 edus per tree, su-cdtb only focuses on news domain and its dds-conversion is relatively easy. as a result, su-cdtb dep keeps a better data consistency than hit-cdtb dep , which may explain why the augmented data promotes the results on hit-cdtb dep , but introduces noise data for su-cdtb dep . as sci-cdtb is from scientific domain and its relations are much different from those of hit-cdtb and su-cdtb, the c c l 2 0 2 1 augmented labeled relations do not bring much improvement on relation labeling. overall, unified dep can serve as a discourse dataset for researching cross-domain or long text discourse parsing. however, our unification method may also introduce noise due to difference in text length and domain, so it remains to be considered how to better leverage this unified large corpus to improve the chinese discourse parsing techniques. ## conclusions in our work, we design semi-automatic methods to unify three chinese discourse corpora with dependency framework. our methods of converting pdtb-style and rst-style discourse annotation into dds can be used to convert more other corpora and further enlarge our unified dataset. at the same time, by implementing several benchmark parsers on the converted data, we find that augmenting training set with the unified data can to some extent improve performance, but may also introduce noise and bring performance loss due to difference in text length and domain. this unified dataset is potentially helpful to research on cross-domain and long text discourse parsing, which will be our future work.