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 +4 more
11/1/2024
cs.CL

No official code found. Best implementation: gucorpling/gum2pdtb (0 stars, last commit 2024-11-04).

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.

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gucorpling/gum2pdtbCommunity100%
01Shell, PythonOct 19, 20241 years ago

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Cite this paper

@article{liu2024gdtb,
  title  = {GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains},
  author = {Yang Janet Liu and Tatsuya Aoyama and Wesley Scivetti and Yilun Zhu and Shabnam Behzad and Lauren Elizabeth Levine and Jessica Lin and Devika Tiwari and Amir Zeldes},
  year   = {2024},
  eprint = {2411.00491},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2411.00491v1}
}

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