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Weerayut Buaphet

Academic Projects
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Thai Nested Named Entity Recognition Corpus

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Thai Nested Named Entity Recognition Corpus.

Weerayut Buaphet, Can Udomcharoenchaikit, Peerat Limkonchotiwat, Attapol Rutherford, and Sarana Nutanong. 2022. In Findings of the Association for Computational Linguistics: ACL 2022, pages 1473–1486, Dublin, Ireland. Association for Computational Linguistics.

Aug 2019 – May 2022

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Mitigating Spurious Correlation in Natural Language Understanding with Counterfactual Inference

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Can Udomcharoenchaikit, Wuttikorn Pornwattanarat, Patomporn Payoungkhamdee, Karunethai Masuk, Weerayut Buaphet, Ekapol Chuangsuwanich, and Sarana Nutanong. 2022. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11308–11321, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

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Mitigating Spurious Correlation in Natural Language Understanding with Counterfactual Inference.

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Can Udomcharoenchaikit, Wuttikorn Ponwitayarat, Patomporn Payoungkhamdee, Kanruethai Masuk, Weerayut Buaphet, Ekapol Chuangsuwanich, and Sarana Nutanong. 2022. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11308–11321, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

Dec 2022

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Cross-Lingual Data Augmentation For Thai Question-Answering.

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Parinthapat Pengpun, Can Udomcharoenchaikit, Weerayut Buaphet, and Peerat Limkonchotiwat. 2023. In Proceedings of the 1st GenBench Workshop on (Benchmarking) Generalisation in NLP, pages 193–203, Singapore. Association for Computational Linguistics.

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Dec 2023

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Few-shot Named Entity Recognition

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In this project, we focus on leveraging the capabilities of large language models to generate relevant examples, thereby enhancing the effectiveness of few-shot NER.

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Under review

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Bi-lingual Named Entity Recognition for financing

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Creating a finance-NER dataset composed of two languages, Thai and English. This project aims to study the knowledge transfer from high-resource to low-resource languages in the financial domain.

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Ongoing project

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