ThaiCoref: Thai Coreference Resolution Dataset

TL;DR

This paper introduces ThaiCoref, a large annotated dataset for Thai coreference resolution, and demonstrates its utility by training models that achieve promising results, addressing the scarcity of Thai NLP resources.

Contribution

The paper presents ThaiCoref, the first large-scale Thai coreference dataset, and applies cross-lingual transfer techniques to improve coreference resolution in Thai.

Findings

Achieved a best F1 score of 67.88% on Thai coreference resolution.

Created a comprehensive dataset with over 777,000 tokens across multiple genres.

Identified linguistic challenges unique to Thai for coreference resolution.

Abstract

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 .