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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.00491 |
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| _version_ | 1866912100083826688 |
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| author | Liu, Yang Janet Aoyama, Tatsuya Scivetti, Wesley Zhu, Yilun Behzad, Shabnam Levine, Lauren Elizabeth Lin, Jessica Tiwari, Devika Zeldes, Amir |
| author_facet | Liu, Yang Janet Aoyama, Tatsuya Scivetti, Wesley Zhu, Yilun Behzad, Shabnam Levine, Lauren Elizabeth Lin, Jessica Tiwari, Devika Zeldes, Amir |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00491 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains Liu, Yang Janet Aoyama, Tatsuya Scivetti, Wesley Zhu, Yilun Behzad, Shabnam Levine, Lauren Elizabeth Lin, Jessica Tiwari, Devika Zeldes, Amir Computation and Language 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. |
| title | GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2411.00491 |