eRST: A Signaled Graph Theory of Discourse Relations and Organization
This work addresses the problem of computational discourse analysis for researchers and practitioners by providing a more comprehensive framework and tools, though it appears incremental as it builds on Rhetorical Structure Theory.
The authors tackled limitations in existing discourse analysis frameworks by proposing Enhanced Rhetorical Structure Theory (eRST), a new theoretical framework that includes discourse relation graphs with features like tree-breaking and explicit signals, and they created an annotated English corpus with over 200K tokens across 12 genres.
In this article we present Enhanced Rhetorical Structure Theory (eRST), a new theoretical framework for computational discourse analysis, based on an expansion of Rhetorical Structure Theory (RST). The framework encompasses discourse relation graphs with tree-breaking, non-projective and concurrent relations, as well as implicit and explicit signals which give explainable rationales to our analyses. We survey shortcomings of RST and other existing frameworks, such as Segmented Discourse Representation Theory (SDRT), the Penn Discourse Treebank (PDTB) and Discourse Dependencies, and address these using constructs in the proposed theory. We provide annotation, search and visualization tools for data, and present and evaluate a freely available corpus of English annotated according to our framework, encompassing 12 spoken and written genres with over 200K tokens. Finally, we discuss automatic parsing, evaluation metrics and applications for data in our framework.