A Complete Shift-Reduce C hinese Discourse Parser with Robust Dynamic Oracle (2020)

Abstract: This work proposes a standalone, complete Chinese discourse parser for practical applications. We approach Chinese discourse parsing from a variety of aspects and improve the shift-reduce parser not only by integrating the pre-trained text encoder, but also by employing novel training strategies. We revise the dynamic-oracle procedure for training the shift-reduce parser, and apply unsupervised data augmentation to enhance rhetorical relation recognition. Experimental results show that our Chinese discourse parser achieves the state-of-the-art performance.

## introduction discourse parsing is one of the fundamental tasks in natural language processing (nlp). typical types of discourse parsing include hierarchical discourse parsing and shallow discourse parsing. the former is aimed at finding the relationships among a series of neighboring elementary discourse units (edus) and further building up a hierarchical tree structure @xcite . instead of establishing a tree structure, the latter finds the across-paragraph relations between all text units in a paragraph or a document. based on rhetorical structure theory discourse treebank (rst-dt) @xcite , hierarchical discourse parsing in english has been well-studied. this paper focuses on hierarchical discourse parsing in chinese. previous work on hierarchical chinese discourse parsing is mostly based on the rst-style chinese discourse treebank @xcite . to distinguish from the other chinese discourse treebank @xcite , which is annotated with the pdtb-style for shallow discourse parsing, we use the term cdtb-14 to refer to the rst-style one and the term cdtb-12 to refer to the pdtb-style one. @xcite propose a pipeline framework and generate the discourse parsing tree in a bottom-up way. @xcite propose an end-to-end system based on a recursive neural network (rvnn) to construct the parsing tree with a cky-like algorithm. @xcite use transition-based method with the stack augmented parser-interpreter neural network (spinn) @xcite as the backbone model, helping their model make a better prediction with the previous information. in this work, we attempt to construct a complete chinese discourse parser, which supports all the four sub-tasks in hierarchical discourse parsing, including edu segmentation, tree structure construction, nuclearity labeling, and rhetorical relation recognition. given a paragraph, our parser extracts all edus, builds the tree structure, identifies the nucleuses, and recognizes the rhetorical relations of all internal nodes. we propose a revised dynamic-oracle procedure @xcite for training the shift-reduce parser. because of the limited training instances in cdtb-14, we also address the data sparsity issue by introducing unsupervised data augmentation @xcite . experimental results show that our methodology is effective, and our model outperforms all the previous models. the contributions of this work are three-fold shown as follows. figure 1 gives an overview of our parser. five stages are performed to transform a raw document into a parse tree: edu segmentation, tree structure construction, rhetorical relation and nuclearity classification, binary tree conversion, and beam search. ## conclusion this work proposes a standalone, complete chinese discourse parser. we integrate bert, uda, and a revised training procedure for constructing a robust shift-reduce parser. our model is compared with a number of previous models, and experimental results show that our model achieves the stateof-the-art performance and is highly competitive with different setups. we will explore cross-lingual transfer learning for supporting more languages.