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

Patrick Huber, Giuseppe Carenini


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.
Anthology ID:
2020.emnlp-main.603
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Editors:
Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7442–7457
Language:
URL:
https://aclanthology.org/2020.emnlp-main.603
DOI:
10.18653/v1/2020.emnlp-main.603
Bibkey:
Cite (ACL):
Patrick Huber and Giuseppe Carenini. 2020. MEGA RST Discourse Treebanks with Structure and Nuclearity from Scalable Distant Sentiment Supervision. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7442–7457, Online. Association for Computational Linguistics.
Cite (Informal):
MEGA RST Discourse Treebanks with Structure and Nuclearity from Scalable Distant Sentiment Supervision (Huber & Carenini, EMNLP 2020)
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PDF:
https://aclanthology.org/2020.emnlp-main.603.pdf
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