ThaiCoref: Thai Coreference Resolution Dataset
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910479401615360 |
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| author | Trakuekul, Pontakorn Leong, Wei Qi Polpanumas, Charin Sawatphol, Jitkapat Tjhi, William Chandra Rutherford, Attapol T. |
| author_facet | Trakuekul, Pontakorn Leong, Wei Qi Polpanumas, Charin Sawatphol, Jitkapat Tjhi, William Chandra Rutherford, Attapol T. |
| contents | While coreference resolution is a well-established research area in Natural Language Processing (NLP), research focusing on Thai language remains limited due to the lack of large annotated corpora. In this work, we introduce ThaiCoref, a dataset for Thai coreference resolution. Our dataset comprises 777,271 tokens, 44,082 mentions and 10,429 entities across four text genres: university essays, newspapers, speeches, and Wikipedia. Our annotation scheme is built upon the OntoNotes benchmark with adjustments to address Thai-specific phenomena. Utilizing ThaiCoref, we train models employing a multilingual encoder and cross-lingual transfer techniques, achieving a best F1 score of 67.88\% on the test set. Error analysis reveals challenges posed by Thai's unique linguistic features. To benefit the NLP community, we make the dataset and the model publicly available at http://www.github.com/nlp-chula/thai-coref . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06000 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ThaiCoref: Thai Coreference Resolution Dataset Trakuekul, Pontakorn Leong, Wei Qi Polpanumas, Charin Sawatphol, Jitkapat Tjhi, William Chandra Rutherford, Attapol T. Computation and Language While coreference resolution is a well-established research area in Natural Language Processing (NLP), research focusing on Thai language remains limited due to the lack of large annotated corpora. In this work, we introduce ThaiCoref, a dataset for Thai coreference resolution. Our dataset comprises 777,271 tokens, 44,082 mentions and 10,429 entities across four text genres: university essays, newspapers, speeches, and Wikipedia. Our annotation scheme is built upon the OntoNotes benchmark with adjustments to address Thai-specific phenomena. Utilizing ThaiCoref, we train models employing a multilingual encoder and cross-lingual transfer techniques, achieving a best F1 score of 67.88\% on the test set. Error analysis reveals challenges posed by Thai's unique linguistic features. To benefit the NLP community, we make the dataset and the model publicly available at http://www.github.com/nlp-chula/thai-coref . |
| title | ThaiCoref: Thai Coreference Resolution Dataset |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2406.06000 |