Domain Adaptation of Thai Word Segmentation Models using Stacked Ensemble
Peerat Limkonchotiwat, Wannaphong Phatthiyaphaibun, Raheem Sarwar, Ekapol Chuangsuwanich, Sarana Nutanong
Abstract
Like many Natural Language Processing tasks, Thai word segmentation is domain-dependent. Researchers have been relying on transfer learning to adapt an existing model to a new domain. However, this approach is inapplicable to cases where we can interact with only input and output layers of the models, also known as “black boxes”. We propose a filter-and-refine solution based on the stacked-ensemble learning paradigm to address this black-box limitation. We conducted extensive experimental studies comparing our method against state-of-the-art models and transfer learning. Experimental results show that our proposed solution is an effective domain adaptation method and has a similar performance as the transfer learning method.- Anthology ID:
- 2020.emnlp-main.315
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3841–3847
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.315
- DOI:
- 10.18653/v1/2020.emnlp-main.315
- Cite (ACL):
- Peerat Limkonchotiwat, Wannaphong Phatthiyaphaibun, Raheem Sarwar, Ekapol Chuangsuwanich, and Sarana Nutanong. 2020. Domain Adaptation of Thai Word Segmentation Models using Stacked Ensemble. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3841–3847, Online. Association for Computational Linguistics.
- Cite (Informal):
- Domain Adaptation of Thai Word Segmentation Models using Stacked Ensemble (Limkonchotiwat et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2020.emnlp-main.315.pdf
- Code
- mrpeerat/SEFR_CUT