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            <title>Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop)</title>
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            <namePart type="given">Selene</namePart>
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            <namePart type="given">Sai</namePart>
            <namePart type="given">Ashish</namePart>
            <namePart type="family">Somayajula</namePart>
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    <abstract>This study proposes a method for learning subword correspondences in parallel sentence pairs using the EM algorithm. Conventional neural machine translation typically employs subword segmentation models trained. However, since existing methods do not consider parallel relationships, inconsistencies in word segmentation between source and target languages may hinder translation model training. Our approach leverages direct modeling of subword correspondences in parallel corpora, thereby improving segmentation consistency across languages. Experiments across multiple machine translation tasks confirm that our proposed method improves translation accuracy for many tasks.</abstract>
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%0 Conference Proceedings
%T Probabilistic Bilingual Subword Segmentation with Latent Subword Alignment
%A Nishida, Shoto
%A Matsui, Daiki
%A Ninomiya, Takashi
%A Goto, Isao
%A Tamura, Akihiro
%Y Baez Santamaria, Selene
%Y Somayajula, Sai Ashish
%Y Yamaguchi, Atsuki
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-383-8
%F nishida-etal-2026-probabilistic
%X This study proposes a method for learning subword correspondences in parallel sentence pairs using the EM algorithm. Conventional neural machine translation typically employs subword segmentation models trained. However, since existing methods do not consider parallel relationships, inconsistencies in word segmentation between source and target languages may hinder translation model training. Our approach leverages direct modeling of subword correspondences in parallel corpora, thereby improving segmentation consistency across languages. Experiments across multiple machine translation tasks confirm that our proposed method improves translation accuracy for many tasks.
%U https://aclanthology.org/2026.eacl-srw.40/
%P 528-534

Markdown (Informal)

[Probabilistic Bilingual Subword Segmentation with Latent Subword Alignment](https://aclanthology.org/2026.eacl-srw.40/) (Nishida et al., EACL 2026)

• Probabilistic Bilingual Subword Segmentation with Latent Subword Alignment (Nishida et al., EACL 2026)

ACL

• Shoto Nishida, Daiki Matsui, Takashi Ninomiya, Isao Goto, and Akihiro Tamura. 2026. Probabilistic Bilingual Subword Segmentation with Latent Subword Alignment. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 528–534, Rabat, Morocco. Association for Computational Linguistics.