Easy-First Bottom-Up Discourse Parsing via Sequence Labelling (2022)

Abstract: We propose a novel unconstrained bottom-up approach for rhetorical discourse parsing based on sequence labelling of adjacent pairs of discourse units (DUs), based on the framework of Koto et al. (2021). We describe the unique training requirements of an unconstrained parser, and explore two different training procedures: (1) fixed left-to-right; and (2) random order in tree construction. Additionally, we introduce a novel dynamic oracle for unconstrained bottom-up parsing. Our proposed parser achieves competitive results for bottom-up rhetorical discourse parsing.

## introduction discourse analysis aims to explain the relationship of texts beyond sentence boundaries, and has been modelled based on rhetorical structure theory (rst: @xcite ). in the rst framework, texts are modelled as a labelled hierarchy of discourse units (du), with elementary discourse units (edu) being the smallest unit (see figure 1 ). although there has been a move from bottomup @xcite @xcite @xcite to top-down approaches @xcite @xcite , we argue that the bottom-up paradigm is conceptually intuitive as humans analyse the structure of documents incrementally based on elementary structures. furthermore, in contemporaneous work, @xcite have shown that bottom-up parsers built on a language model pre-trained at the edu level outperform top-down parsers trained comparably. in this paper, we revisit the bottom-up approach and introduce a novel unconstrained bottom-up discourse parsing o(n 2 ) by adopting the sequencelabelling framework of @xcite . unconstrained means that we relax the fixed left-to-right direction of discourse tree construction, allowing us to make the easiest decisions first. intuitively speaking, when it comes to making the harder decisions, the history of existing structures can be used to make more reliable predictions. @xcite introduced the non-directional easy-first algorithm to dependency parsing, which is a greedy, best-first parser, which relaxes the left-to-right order constraint of other bottom-up transition-based algorithms @xcite . because the model is conditioned on existing parsed structures, we need to sample parsing trajectories to train the model, and compare two simple sampling methods: (1) left-to-right, and (2) random. to the best of our knowledge, we are the first to propose a bottom-up model for discourse parsing using the easy-first algorithm in a sequence labelling framework. to summarize our contributions: (1) we propose a novel bottom-up context-sensitive parser; (2) we explore sampling methods for training a contextsensitive parser; and (3) we devise a novel dynamic oracle for our unconstrained bottom-up discourse parser. we make the source code available online. foot_0 2 bottom-up rst parsing we construct rst trees in a bottom-up fashion, starting with a sequence of edus and sequentially merging adjacent discourse units. at each stage, there are multiple merge points in the partiallyparsed document that make up the gold discourse tree, and we define all such points to be gold merges. we impose no constraint on which gold merge needs to be executed first. following @xcite , we frame the merging task as a sequence labeling problem. we train a merging model to assign a binary label @xmath0, 1} to each discourse unit, where 1 indicates the unit and its right neighbour are subject to a gold merge. for each parse state, we train the model to label all gold merge points. at test time, we select the highest-probability merge point to construct the next parse state. we assign the discourse label and nuclearity relation separately with a second classifier after a merge is decided. ## conclusion in this work, we adapted the sequence labeling framework to bottom-up rst parsing, introducing an easy-first parser conditioned on past decisions. we investigated methods to sample training examples for a non-directional parser, and proposed a dynamic oracle for our bottom-up parsing. we demonstrated that our parser achieves competitive results for bottom-up rst parsing.