@inproceedings{limkonchotiwat-etal-2020-domain,
title = "Domain Adaptation of {T}hai Word Segmentation Models using Stacked Ensemble",
author = "Limkonchotiwat, Peerat and
Phatthiyaphaibun, Wannaphong and
Sarwar, Raheem and
Chuangsuwanich, Ekapol and
Nutanong, Sarana",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.315",
doi = "10.18653/v1/2020.emnlp-main.315",
pages = "3841--3847",
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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<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.</abstract>
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%0 Conference Proceedings
%T Domain Adaptation of Thai Word Segmentation Models using Stacked Ensemble
%A Limkonchotiwat, Peerat
%A Phatthiyaphaibun, Wannaphong
%A Sarwar, Raheem
%A Chuangsuwanich, Ekapol
%A Nutanong, Sarana
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F limkonchotiwat-etal-2020-domain
%X 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.
%R 10.18653/v1/2020.emnlp-main.315
%U https://aclanthology.org/2020.emnlp-main.315
%U https://doi.org/10.18653/v1/2020.emnlp-main.315
%P 3841-3847
Markdown (Informal)
[Domain Adaptation of Thai Word Segmentation Models using Stacked Ensemble](https://aclanthology.org/2020.emnlp-main.315) (Limkonchotiwat et al., EMNLP 2020)
ACL