MEGA RST Discourse Treebanks with Structure and Nuclearity from Scalable Distant Sentiment Supervision

• URL: http://arxiv.org/abs/2011.03017v1
• Date: Thu, 5 Nov 2020 18:22:38 GMT
• Title: MEGA RST Discourse Treebanks with Structure and Nuclearity from Scalable Distant Sentiment Supervision
• Authors: Patrick Huber and Giuseppe Carenini
• Abstract summary: We present a novel methodology to automatically generate discourse treebanks using distant supervision from sentiment-annotated datasets. Our approach generates trees incorporating structure and nuclearity for documents of arbitrary length by relying on an efficient beam-search strategy. Experiments indicate that a discourse trained on our MEGA-DT treebank delivers promising inter-domain performance gains.
• Score: 30.615883375573432
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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