TableMBR: Minimum Bayes Risk Table Generation Based on Structural Consistency

Daiki Yoshida; Hiroyuki Deguchi; Yusuke Sakai; Hidetaka Kamigaito; Taro Watanabe

TableMBR: Minimum Bayes Risk Table Generation Based on Structural Consistency

Daiki Yoshida, Hiroyuki Deguchi, Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe

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Abstract

The text-to-table task aims to generate structured data in tabular formats from unstructured text. While the integration of large language models (LLMs) has significantly enhanced the comprehensiveness and flexibility of generation, challenges regarding inconsistent output quality persist, such as the inclusion of redundant information and numerical inaccuracies. We propose TableMBR, a robust table generation method that maintains structural consistency through minimum Bayes risk (MBR) decoding. Experimental results showed that TableMBR outperforms the baseline, achieving relative improvements of up to 15% in F1 score on Rotowire and 23% in accuracy on LiveSum.

Anthology ID:: 2026.acl-srw.95
Volume:: Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
Month:: July
Year:: 2026
Address:: San Diego, California, United States
Editors:: Santosh T.Y.S.S., Juan Diego Rodriguez, Ona de Gibert
Venue:: ACL
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 1087–1102
Language:
URL:: https://aclanthology.org/2026.acl-srw.95/
DOI:
Bibkey:
Cite (ACL):: Daiki Yoshida, Hiroyuki Deguchi, Yusuke Sakai, Hidetaka Kamigaito, and Taro Watanabe. 2026. TableMBR: Minimum Bayes Risk Table Generation Based on Structural Consistency. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 1087–1102, San Diego, California, United States. Association for Computational Linguistics.
Cite (Informal):: TableMBR: Minimum Bayes Risk Table Generation Based on Structural Consistency (Yoshida et al., ACL 2026)
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PDF:: https://aclanthology.org/2026.acl-srw.95.pdf

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@inproceedings{yoshida-etal-2026-tablembr,
    title = "{T}able{MBR}: Minimum {B}ayes Risk Table Generation Based on Structural Consistency",
    author = "Yoshida, Daiki  and
      Deguchi, Hiroyuki  and
      Sakai, Yusuke  and
      Kamigaito, Hidetaka  and
      Watanabe, Taro",
    editor = "T.Y.S.S., Santosh  and
      Rodriguez, Juan Diego  and
      de Gibert, Ona",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.acl-srw.95/",
    pages = "1087--1102",
    ISBN = "979-8-89176-393-7",
    abstract = "The text-to-table task aims to generate structured data in tabular formats from unstructured text. While the integration of large language models (LLMs) has significantly enhanced the comprehensiveness and flexibility of generation, challenges regarding inconsistent output quality persist, such as the inclusion of redundant information and numerical inaccuracies. We propose TableMBR, a robust table generation method that maintains structural consistency through minimum Bayes risk (MBR) decoding. Experimental results showed that TableMBR outperforms the baseline, achieving relative improvements of up to 15{\%} in F1 score on Rotowire and 23{\%} in accuracy on LiveSum."
}

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%0 Conference Proceedings
%T TableMBR: Minimum Bayes Risk Table Generation Based on Structural Consistency
%A Yoshida, Daiki
%A Deguchi, Hiroyuki
%A Sakai, Yusuke
%A Kamigaito, Hidetaka
%A Watanabe, Taro
%Y T.Y.S.S., Santosh
%Y Rodriguez, Juan Diego
%Y de Gibert, Ona
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-393-7
%F yoshida-etal-2026-tablembr
%X The text-to-table task aims to generate structured data in tabular formats from unstructured text. While the integration of large language models (LLMs) has significantly enhanced the comprehensiveness and flexibility of generation, challenges regarding inconsistent output quality persist, such as the inclusion of redundant information and numerical inaccuracies. We propose TableMBR, a robust table generation method that maintains structural consistency through minimum Bayes risk (MBR) decoding. Experimental results showed that TableMBR outperforms the baseline, achieving relative improvements of up to 15% in F1 score on Rotowire and 23% in accuracy on LiveSum.
%U https://aclanthology.org/2026.acl-srw.95/
%P 1087-1102

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Markdown (Informal)

[TableMBR: Minimum Bayes Risk Table Generation Based on Structural Consistency](https://aclanthology.org/2026.acl-srw.95/) (Yoshida et al., ACL 2026)

TableMBR: Minimum Bayes Risk Table Generation Based on Structural Consistency (Yoshida et al., ACL 2026)

ACL

Daiki Yoshida, Hiroyuki Deguchi, Yusuke Sakai, Hidetaka Kamigaito, and Taro Watanabe. 2026. TableMBR: Minimum Bayes Risk Table Generation Based on Structural Consistency. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 1087–1102, San Diego, California, United States. Association for Computational Linguistics.