Tree Representations in Transition System for RST Parsing (2020)

Abstract: The transition-based systems in the past studies propose a series of actions, to build a right-heavy binarized tree for the RST parsing. However, the nodes of the binary-nuclear relations (e.g., Contrast) have the same nuclear type with those of the multi-nuclear relations (e.g., Joint) in the binary tree structure. In addition, the reduce action only construct binary trees instead of multi-branch trees, which is the original RST tree structure. In our paper, we design a new nuclear type for the multi-nuclear relations, and a new action to construct a multi-branch tree. We enrich the feature set by extracting additional refined dependency feature of texts from the Bi-Affine model. We also compare the performance of two approaches for RST parsing in the transition-based system: a joint action of reduce-shift and nuclear type (i.e., Reduce-SN) vs a separate one that applies Reduce action first and then assigns nuclear type. We find that the new devised nuclear type and action are more capable of capturing the multi-nuclear relation and the joint action is more suitable than the separate one. Our multi-branch tree structure obtains the state-of-the-art performance for all the 18 coarse relations.

## introduction it is expected that a document consists of text units that are logically connected within the context through discourse relations. text-level discoursing parsing plays a significant role in many nlp downstream task, such as question-answering @xcite , sentiment analysis @xcite and abstractive summarization @xcite . the rhetorical structure theory (rst) @xcite is one of the widely explored text-level discourse parsing theory, which parses a document into a hierarchical discourse tree. the discourse tree is built over its smallest parsing unit, namely, elementary discourse unit (edu), placed in the leaf nodes. the tree's non-terminal nodes bear the information of span, nuclearity and relation. the span represents the cover range of a sequence of edus; while the nuclearity status is the semantic role in a relation (i.e., nucleus or satellite). a text tagged by nucleus is more essential than the satellite. conventionally, there are three nuclearity types including nucleus-satellite (ns), satellite-nucleus (sn) and nucleus-nucleus (nn). as for relation, there are mainly two types of relations: the mono-nuclear relation, such as "attribution" or "summary", with the the nuclearity type being either "ns" or "sn"; and the multi-nuclear relation, such as "contrast" or "same-unit", with the nuclearity types of "nn". among all the parsing systems dedicated to rst parsing, the transition-based systems obtain comparative performance with the cky-like system. the transition-based algorithms are first explored in @xcite , which adopt a sequence of shift-reduce action to construct rhetorical structures of texts. the study of @xcite achieves the best performance among all the transition-based systems, by parsing the discourse tree in two stages, namely, parsing a naked discourse tree (identifying span and nuclearity) in the first stage and assigning relation in the second stage. the researchers use only one classifier for the joint action of shift-reduce and nuclear types (e.g., reduce-ns) to parse the tree. in this this work is licensed under a creative commons attribution 4.0 international license. license details: http:// creativecommons.org/licenses/by/4.0/ . previous transition systems do not differentiate the nuclear type between multi-nuclear relations and binary-nuclear relations. neither did they achieve satisfying performance on the multi-branch relation such as topic-change. we find that there are 1.9% and 1.7% multi-branch nodes in the original rst tree in the training and test dataset respectively (the frequency details are shown in the appendix). because we utilize the nucleus feature @xcite in the relation classifier, a distinctive nuclearity type for the multi-branch relations would be helpful to differentiate them from the binary-branch relations. in addition, previous studies represent rst by the right-heavy binary trees, which inevitably create the redundant nonexistent node in the original rst tree. to solve these problems, we come up with a new nuclear type " n" for the multi-nuclear relation, and a new action " r" to construct a multi-branch tree. experimental results tell us that the newly designed nuclear type and action labels are capable of capturing multi-nuclear relations. in sum, our paper mainly makes the following contributions: ## our approach we construct two tree structures: the binary tree and multi-branch tree structures, with a new nuclear type and a new action, respectively. to enrich the features for the small number of training examples of the new labels, we add the extra dependency features generated by the bi-affine model @xcite , along with the features extracted by @xcite . we also examine the performance of two approaches in our transition-based system: to identify span and nuclearity concurrently with one classifier vs to identify them separately using different classifiers. ## experimental setup we use the default 385 documents (347 and 38 documents in training and test dataset respectively) from the wall street journal (rst-dt) @xcite . the 18 coarse-grained relations are evaluated in our experiment such as the binary-nuclear relation of "contrast" and the multi-nuclear relation of "joint" @xcite . we report the micro-averaged f1 scores of rst-parseval @xcite and labelled attachment decisions (las) with respect to span, nuclearity and relation. ## results we only compare our results with the state-of-the-art transition-based system: two-stages system @xcite . as shown in