Papers by Wannaphong Phatthiyaphaibun
Robust Fragment-Based Framework for Cross-lingual Sentence Retrieval (2021.findings-emnlp)
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Nattapol Trijakwanich, Peerat Limkonchotiwat, Raheem Sarwar, Wannaphong Phatthiyaphaibun, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Cross-lingual Sentence Retrieval (CLSR) aims at retrieving parallel sentence pairs that are translations of each other from a multilingual set of comparable documents. |
| Approach: | They propose a framework for cross-lingual sentence retrieval that uses a collection of fragments to improve sentence retrievals. |
| Outcome: | The proposed framework improves the retrieval robustness of the base sentences encoded by m-USE, LASER, and LaBSE. |
Handling Cross- and Out-of-Domain Samples in Thai Word Segmentation (2021.findings-acl)
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Peerat Limkonchotiwat, Wannaphong Phatthiyaphaibun, Raheem Sarwar, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Word segmentation is domain-dependent, which can be a challenge in low-resource languages like Thai and Urdu . a framework to handle out-of-domain inputs is proposed to improve word segmentation . |
| Approach: | They propose a domaingeneric domain adaptation framework and data augmentation technique to combat low-resource problems. |
| Outcome: | The proposed model outperforms the state-of-the-art Thai word segmentation method in out-of domain scenarios. |
Domain Adaptation of Thai Word Segmentation Models using Stacked Ensemble (2020.emnlp-main)
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Peerat Limkonchotiwat, Wannaphong Phatthiyaphaibun, Raheem Sarwar, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Thai word segmentation is domain-dependent, and researchers have been relying on transfer learning to adapt existing models to new domains. |
| Approach: | They propose a filter-and-refine solution to address Thai word segmentation as a domain-dependent problem. |
| Outcome: | The proposed method is an effective domain adaptation method and has similar performance as the transfer learning method. |