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Domain adaptation of Thai word segmentation models using stacked ensemble
Limkonchotiwat, Peerat Phatthiyaphaibun, Wannaphong Sarwar, Raheem Chuangsuwanich, Ekapol Nutanong, Sarana
Limkonchotiwat, Peerat
Phatthiyaphaibun, Wannaphong
Sarwar, Raheem
Chuangsuwanich, Ekapol
Nutanong, Sarana
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2020-11-12
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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.
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Limkonchotiwat, P., Phatthiyaphaibun, W., Sarwar, R., Chuangsuwanich, E. and Nutanong, S. (2020) Domain adaptation of Thai word segmentation models using stacked ensemble, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 16th–20th November, 2020, pp. 3841–3847.
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en
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© 2020. Published by ACL. This is an open access article available under a Creative Commons licence.
The published version can be accessed at the following link on the publisher’s website: https://www.aclweb.org/anthology/2020.emnlp-main.315/
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9781952148606