Discourse Parsing Enhanced by Discourse Dependence Perception (2022)

Abstract: In recent years, top-down neural models have achieved significant success in text-level discourse parsing. Nevertheless, they still suffer from the top-down error propagation issue, especially when the performance on the upper-level tree nodes is terrible. In this research, we aim to learn from the correlations in between EDUs directly to shorten the hierarchical distance of the RST structure to alleviate the above problem. Specifically, we contribute a joint top-down framework that learns from both discourse dependency and constituency parsing through one shared encoder and two independent decoders. Moreover, we also explore a constituency-to-dependency conversion scheme tailored for the Chinese discourse corpus to ensure the high quality of the joint learning process. Our experimental results on CDTB show that the dependency information we use well heightens the understanding of the rhetorical structure, especially for the upper-level tree layers.

## introduction according to the representative rhetorical structure theory (rst) @xcite , a text can be presented as a hierarchical discourse tree (dt) built on a set of elementary discourse units (edus). given a piece of text, rst-style discourse parsing identifies such a dt with edus serving as terminal nodes. moreover, it labels the rhetorical relations and nuclearity attributes associated with each non-terminal node of the dt. due to its far-reaching effects on text understanding and downstream nlp applications, text-level discourse parsing has been drawing more and more attention in the past decade. from the early bottom-up approaches @xcite @xcite @xcite @xcite to ⇤ corresponding author the more recent top-down frameworks @xcite @xcite @xcite , previous studies gradually switch from feature-based machine learning methods to deep neural models and have achieved particular success. among current neural models, top-down parsers, in most cases, perform better than bottom-up ones due to their capability of capturing global context information. nevertheless, due to the long-distance dependencies in between textual units and the notorious lack of training data, top-down text-level discourse parsing still faces the following possible bottlenecks: facing the above challenges, some recent studies have done certain preliminary explorations, hoping to improve top-down parsing by expanding the original small-scale training data @xcite or introducing global optimization objectives @xcite . unlike previous work, we aim to improve the accuracy of upper-level node prediction to reduce error propagation for better rst parsing performance. to achieve this goal, we set our sights on discourse-level dependencies, aiming at employing the dependencies in between edus to dig out clues hidden within those head edus that are conducive to the understanding of rhetorical structures. specifically, we cast discourse constituency tree (dct) parsing as the main task and discourse dependency tree (ddt) parsing as the auxiliary one and joint the two tasks through one shared encoder and two different decoders. in this way, on the one hand, we enhance the edu representation with multi-task knowledge through the shared edu encoder. on the other, since the converted ddts derive from the manually annotated dcts, perceiving the dependencies between edus will conversely stimulate the dct parsing model to produce better results, especially for the upper-level dt nodes foot_0 . ## related work in the literature, previous work on discourse parsing can be classified into two categories: bottom-up and top-down approaches. for a long time, many researchers manually exploited various lexical, syntactic, and semantic features @xcite @xcite or automatically captured hidden information @xcite to compute the probability distribution of relations between two adjacent discourse units (dus) and then selected the two units with the highest probability to merge into an upper-level unit. recursively in this way, a discourse constituency tree is created from bottom to up. besides, there are also some studies that cast rst parsing as a transition action determination process, where the discourse parser makes shift or reduce action decisions in a greedy way to determine whether to merge the current two dus or not @xcite @xcite . until recent years, top-down neural architectures gained much more popularity. in the literature, @xcite proposed the first top-down sentencelevel discourse parser based on pointer nets, which operates in a linear time. zhang et al. (2020; 2021 ) cast text-level discourse parsing as a top-down split point ranking process and introduced an adversarial method to optimize the parsing steps from a global perspective. @xcite proposed parsing a document in three levels of granularity (i.e., document-level, paragraph-level, and sentence-level) and further introduced a semisupervised method to extend the original rst-dt corpus for performance improvement. notably, some recent studies also proved the effectiveness of pre-trained language models on discourse parsing @xcite . in general, compared with bottom-up parsing, current top-down parsers obtain more outstanding performance since they benefit from the global information of the entire article. however, the global context information is known to be multifarious and complicated. it is challenging for the top-down parsers to grasp all the textual details accurately, especially at the initial stage of parsing, which may aggravate the issue of top-down error propagation. in this work, we build our parser based on the top-down framework of @xcite and explore tackling the above problem via discourse dependency information. ## motivation in order to make better choices at the initial stage of discourse parsing to lay a good foundation for succedent parsing of subtrees, we consider incorporating discourse-level dependencies. to support our argument, we present an example in figure 1 where @xcite shows a native dct tree foot_2 and figure (b) shows the converted ddt structure corresponding to the tree. subsequently, our motivation comes from the following two observations: in short, the converted dependency arcs can help reduce the complexity of dct trees to some extent, and the more direct connections between edus could provide valuable clues for better parsing performance, especially for the upper-layer tree nodes with a deep hierarchy. on this basis, we propose a multi-task learning approach to jointly learn dct and ddt parsing, aiming to enhance the discourse representation via discourse dependencies for a better understanding of the rhetorical structure. ## joint dct and ddt parsing adopting the multi-task strategy, our model simultaneously conducts discourse constituency parsing and discourse dependency parsing by sharing the edu representations, where discourse constituency parsing is the main task, and discourse dependency parsing serves as the auxiliary one. the whole architecture can be framed as an encoder-decoder model that contains one encoder and two different decoders, as illustrated in figure 2 . ## conclusion this paper contributes a multi-task learning architecture that jointly learns discourse-level constituency and dependency parsing through one shared encoder and two independent decoding modules. moreover, we introduce a constituency-todependency conversion method tailored for the chinese corpus to ensure the quality of the joint learning process. the experimental results on the cdtb corpus show that the discourse dependency information is efficient in improving the performance of discourse constituency parsing on all metrics, especially for the upper-level tree layers. the results of this paper show that the use of textual knowledge such as rhetorical dependencies can effectively improve the machine's understanding of discourse parsing. inspired by this, in our future work, we will explore the use of meta-learning techniques to learn the knowledge of dependencies such as reference chains and topic chains to achieve the ability to parse various discourse dependency structures including the rhetorical dependencies.