GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains
Yang Janet Liu, Tatsuya Aoyama, Wesley Scivetti, Yilun Zhu, Shabnam Behzad, Lauren Elizabeth Levine, Jessica Lin, Devika Tiwari
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.
BibTeX
@inproceedings{liu-etal-2024-gdtb,
title = "{GDTB}: Genre Diverse Data for {E}nglish Shallow Discourse Parsing across Modalities, Text Types, and Domains",
author = "Liu, Yang Janet and
Aoyama, Tatsuya and
Scivetti, Wesley and
Zhu, Yilun and
Behzad, Shabnam and
Levine, Lauren Elizabeth and
Lin, Jessica and
Tiwari, Devika and
Zeldes, Amir",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.684/",
doi = "10.18653/v1/2024.emnlp-main.684",
pages = "12287--12303"
}