W-RST: Towards a Weighted RST-style Discourse Framework

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pages 3908–3918 August 1–6, 2021. ©2021 Association for Computational Linguistics 3908 W-RST: Towards a Weighted RST-style Discourse Framework Patrick Huber∗, Wen Xiao∗, Giuseppe Carenini Department of Computer Science University of British Columbia Vancouver, BC, Canada, V6T 1Z4 {huberpat, xiaowen3, carenini}@cs.ubc.ca Abstract Aiming for a better integration of data-driven and linguistically-inspired approaches, we ex- plore whether RST Nuclearity, assigning a bi- nary assessment of importance between text segments, can be replaced by automatically generated, real-valued scores, in what we call a Weighted-RST framework. In particular, we find that weighted discourse trees from aux- iliary tasks can benefit key NLP downstream applications compared to nuclearity-centered approaches. We further show that real-valued importance distributions partially and interest- ingly align with the assessment and uncer- tainty of human annotators. 1 Introduction Ideally, research in Natural Language Processing (NLP) should balance and integrate findings from machine learning approaches with insights and the- ories from linguistics. With the enormous success of data-driven approaches over the last decades, this balance has arguably and excessively shifted, with linguistic theories playing a less and less crit- ical role. Even more importantly, there are only little attempts made to improve such theories in light of recent empirical results. In the context of discourse, two main theories have emerged in the past: The Rhetorical Structure Theory (RST) (Carlson et al., 2002) and PDTB (Prasad et al., 2008). In this paper, we focus on RST, exploring whether the underlying theory can be refined in a data-driven manner. In general, RST postulates a complete discourse tree for a given document. To obtain this formal representation as a projective consituency tree, a given document is first separated into so called El- ementary Discourse Units (or short EDUs), repre- senting clause-like sentence fragments of the input ∗Equal contribution. document. Afterwards, the discourse tree is built by hierarchically aggregating EDUs into larger con- stituents annotated with an importance indicator (in RST called nuclearity) and a relation holding between siblings in the aggregation. The nuclear- ity attribute in RST thereby assigns each sub-tree either a nucleus-attribute, indicating central impor- tance of the sub-tree in the context of the document, or a satellite-attribute, categorizing the sub-tree as of peripheral importance. The relation attribute fur- ther characterizes the connection between sub-trees (e.g. Elaboration, Cause, Contradiction). One central requirement of the RST discourse theory, as for all linguistic theories, is that a trained human should be able to specify and interpret the discourse representations. While this is a clear advantage when trying to generate explainable outcomes, it also introduces problematic, human- centered simplifications; the most radical of which is arguably the nuclearity attribute, indicating the importance among siblings. Intuitively, such a coarse (binary) importance assessment does not allow to represent nuanced dif- ferences regarding sub-tree importance, which can potentially be critical for downstream tasks. For instance, the importance of two nuclei siblings is rather intuitive to interpret. However, …

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