MEGA RST Discourse Treebanks with Structure and Nuclearity from Scalable Distant Sentiment Supervision
This addresses the problem of limited data for discourse parsing in NLP, enabling more robust data-driven approaches, though it is incremental as it builds on existing distant supervision techniques.
The authors tackled the lack of large discourse treebanks for RST-style parsing by developing a scalable method to automatically generate MEGA-DT, a new large-scale corpus using distant sentiment supervision, resulting in a parser trained on it showing promising inter-domain performance gains compared to those trained on human-annotated corpora.
The lack of large and diverse discourse treebanks hinders the application of data-driven approaches, such as deep-learning, to RST-style discourse parsing. In this work, we present a novel scalable methodology to automatically generate discourse treebanks using distant supervision from sentiment-annotated datasets, creating and publishing MEGA-DT, a new large-scale discourse-annotated corpus. Our approach generates discourse trees incorporating structure and nuclearity for documents of arbitrary length by relying on an efficient heuristic beam-search strategy, extended with a stochastic component. Experiments on multiple datasets indicate that a discourse parser trained on our MEGA-DT treebank delivers promising inter-domain performance gains when compared to parsers trained on human-annotated discourse corpora.