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<p>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 <i>ThaiCoref</i>, 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 and cover more cases. 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. Our error analysis reveals challenges posed by Thai’s unique morphological and syntactic features. To benefit the NLP community, we make the dataset and the model publicly available at <a href="http://www.github.com/nlp-chula/thai-coref">http://www.github.com/nlp-chula/thai-coref</a>.</p>

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ThaiCoref: Thai coreference resolution dataset

  • 期刊论文

  • 发表日期：

    2026年1月8日

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  • Pontakorn Trakuekul,
  • Wei Qi Leong,
  • Charin Polpanumas,
  • Jitkapat Sawatphol,
  • William Chandra Tjhi,
  • Attapol T. Rutherford

摘要

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 and cover more cases. 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. Our error analysis reveals challenges posed by Thai’s unique morphological and syntactic features. To benefit the NLP community, we make the dataset and the model publicly available at http://www.github.com/nlp-chula/thai-coref.