W - RST : Towards a Weighted RST -style Discourse Framework (2021)

Abstract: Aiming for a better integration of data-driven and linguistically-inspired approaches, we explore whether RST Nuclearity, assigning a binary 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 auxiliary tasks can benefit key NLP downstream applications, compared to nuclearity-centered approaches. We further show that real-valued importance distributions partially and interestingly align with the assessment and uncertainty of human annotators.

## introduction ideally, research in natural language processing (nlp) should balance and integrate findings from machine learning approaches with insights and theories 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 critical 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) @xcite and pdtb @xcite . 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 elementary discourse units (or short edus), representing clause-like sentence fragments of the input document. afterwards, the discourse tree is built by hierarchically aggregating edus into larger constituents annotated with an importance indicator (in rst called nuclearity) and a relation holding between siblings in the aggregation. the nuclearity attribute in rst thereby assigns each sub-tree either a nucleus-attribute, indicating central importance 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 further 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, humancentered 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 differences 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, having siblings annotated as "nucleus-satellite" or "satellitenucleus" leaves the question on how much more important the nucleus sub-tree is compared to the satellite, as shown in figure 1 . in general, it is unclear (and unlikely) that the actual importance distributions between siblings with the same nuclearity attribution are consistent. based on this observation, we investigate the potential of replacing the binary nuclearity assessment postulated by rst with automatically generated, real-valued importance scores in a new, weighted-rst framework. in contrast with previous work that has assumed rst and developed computational models of discourse by simply applying machine learning methods to rst annotated treebanks @xcite @xcite @xcite , we rely on very recent empirical studies showing that weighted "silver-standard" discourse trees can be inferred from auxiliary tasks such as sentiment analysis @xcite and summarization @xcite . in our evaluation, we assess both, computational benefits and linguistic insights. in particular, we find that automatically generated, weighted discourse trees can benefit key nlp downstream tasks. we further show that real-valued importance scores (at least partially) align with human annotations and can interestingly also capture uncertainty in human annotators, implying some alignment of the importance distributions with linguistic ambiguity. ## related work first introduced by @xcite , the rhetorical structure theory (rst) has been one of the primary guiding theories for discourse analysis @xcite @xcite @xcite , discourse parsing @xcite @xcite @xcite , and text planning @xcite @xcite . the rst framework thereby comprehensively describes the organization of a document, guided by the author's communicative goals, encompassing three components: (1) a projective constituency tree structure, often referred to as the tree span. (2) a nuclearity attribute, assigned to every internal node of the discourse tree, encoding relative importance between the nodes' sub-trees, with the nucleus expressing primary importance and a satellite signifying supplementary sub-trees. (3) a relation attribute for every internal node describing the relationship between the sub-trees of a node (e.g., contrast, evidence, contradiction). arguably, the weakest aspect of an rst representation is the nuclearity assessment, which makes a too coarse differentiation between primary and secondary importance of sub-trees. however, despite its binary assignment of importance and even though the nuclearity attribute is only one of three components of an rst tree, it has major implications for many downstream tasks, as already shown early on by @xcite , using the nuclearity attribute as the key signal in extractive summarization. further work in sentiment analysis @xcite also showed the importance of nuclearity for the task by first converting the constituency tree into a dependency tree (more aligned with the nuclearity attribute) and then using that tree to predict sentiment more accurately. both of these results indicate that nuclearity, even in the coarse rst version, already contains valuable information. hence, we believe that this coarsegrained classification is reasonable when manually annotating discourse, but see it as a major point of improvement, if a more fine-grained assessment could be correctly assigned. we therefore explore the potential of assigning a weighted nuclearity attribute in this paper. while plenty of studies have highlighted the important role of discourse for real-world downstream tasks, including summarization, @xcite @xcite , sentiment analysis @xcite @xcite ) and text classification @xcite , more critical to our approach is very recent work exploring such connection in the opposite direction. in @xcite , we exploit sentiment related information to generate "silver-standard" nuclearity annotated discourse trees, showing their potential on the domain-transfer discourse parsing task. crucially for our purposes, this approach internally generates real-valued importance-weights for trees. for the task of extractive summarization, we follow our intuition given in @xcite and @xcite , exploiting the connection between summarization and discourse. in particular, in @xcite , we demonstrate that the selfattention matrix learned during the training of a transformer-based summarizer captures valid aspects of constituency and dependency discourse trees. to summarize, building on our previous work on creating discourse trees through distant supervision, we take a first step towards generating weighted discourse trees from the sentiment analysis and summarization tasks. ## w-rst treebank generation given the intuition from above, we combine information from machine learning approaches with insights from linguistics, replacing the humancentered nuclearity assignment with real-valued weights obtained from the sentiment analysis and summarization tasks foot_0 . an overview of the process to generate weighted rst-style discourse trees is shown in figure 2 , containing the training phase (left) and the w-rst discourse inference phase (center) described here. the w-rst discourse evaluation (right), is covered in section 4. ## w-rst discourse evaluation to assess the potential of w-rst, we consider two evaluation scenarios (figure 2 , right): (1) apply weighted discourse trees to the tasks of sentiment analysis and summarization and ( 2 ) analyze the weight alignment with human annotations. ## conclusion and future work we propose w-rst as a new discourse framework, where the binary nuclearity assessment postulated by rst is replaced with more expressive weights, that can be automatically generated from auxiliary tasks. a series of experiments indicate that w-rst is beneficial to the two key nlp downstream tasks of sentiment analysis and summarization. further, we show that w-rst trees interestingly align with the uncertainty of human annotations. for the future, we plan to develop a neural discourse parser that learns to predict importance weights instead of nuclearity attributions when trained on large w-rst treebanks. more longer term, we want to explore other aspects of rst that can be refined in light of empirical results, plan to integrate our results into state-of-the-art sentiment analysis and summarization approaches (e.g. @xcite ) and generate parallel w-rst structures in a multi-task manner to improve the generality of the discourse trees.