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        <title>Character-based Thai Word Segmentation with Multiple Attentions</title>
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        <namePart type="given">Thodsaporn</namePart>
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        <namePart type="family">Kamigaito</namePart>
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        <namePart type="given">Manabu</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
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    <abstract>Character-based word-segmentation models have been extensively applied to agglutinative languages, including Thai, due to their high performance. These models estimate word boundaries from a character sequence. However, a character unit in sequences has no essential meaning, compared with word, subword, and character cluster units. We propose a Thai word-segmentation model that uses various types of information, including words, subwords, and character clusters, from a character sequence. Our model applies multiple attentions to refine segmentation inferences by estimating the significant relationships among characters and various unit types. The experimental results indicate that our model can outperform other state-of-the-art Thai word-segmentation models.</abstract>
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        <url>https://aclanthology.org/2021.ranlp-1.31</url>
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        <date>2021-09</date>
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%0 Conference Proceedings
%T Character-based Thai Word Segmentation with Multiple Attentions
%A Chay-intr, Thodsaporn
%A Kamigaito, Hidetaka
%A Okumura, Manabu
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F chay-intr-etal-2021-character
%X Character-based word-segmentation models have been extensively applied to agglutinative languages, including Thai, due to their high performance. These models estimate word boundaries from a character sequence. However, a character unit in sequences has no essential meaning, compared with word, subword, and character cluster units. We propose a Thai word-segmentation model that uses various types of information, including words, subwords, and character clusters, from a character sequence. Our model applies multiple attentions to refine segmentation inferences by estimating the significant relationships among characters and various unit types. The experimental results indicate that our model can outperform other state-of-the-art Thai word-segmentation models.
%U https://aclanthology.org/2021.ranlp-1.31
%P 264-273

Markdown (Informal)

[Character-based Thai Word Segmentation with Multiple Attentions](https://aclanthology.org/2021.ranlp-1.31) (Chay-intr et al., RANLP 2021)

• Character-based Thai Word Segmentation with Multiple Attentions (Chay-intr et al., RANLP 2021)

ACL

• Thodsaporn Chay-intr, Hidetaka Kamigaito, and Manabu Okumura. 2021. Character-based Thai Word Segmentation with Multiple Attentions. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 264–273, Held Online. INCOMA Ltd..