GDTB: Genre Diverse Data for English Shallow Discourse Parsing Across Modalities, Text Types, and Domains

MCML Authors

Yang Janet Liu

* Former Member

→ Group Barbara Plank
 AI and Computational Linguistics

Abstract

Work on shallow discourse parsing in English has focused on the Wall Street Journal corpus, the only large-scale dataset for the language in the PDTB framework. However, the data is not openly available, is restricted to the news domain, and is by now 35 years old. In this paper, we present and evaluate a new open-access, multi-genre benchmark for PDTB-style shallow discourse parsing, based on the existing UD English GUM corpus, for which discourse relation annotations in other frameworks already exist. In a series of experiments on cross-domain relation classification, we show that while our dataset is compatible with PDTB, substantial out-of-domain degradation is observed, which can be alleviated by joint training on both datasets.

inproceedings

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EMNLP 2024

Conference on Empirical Methods in Natural Language Processing. Miami, FL, USA, Nov 12-16, 2024.

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Authors

Y. J. Liu • T. Aoyama • W. Scivetti • Y. Zhu • S. Behzad • L. E. Levine • J. Lin • D. Tiwari • A. Zeldes

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Links

DOI

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Research Area

 B2 | Natural Language Processing