@inproceedings{lee-etal-2026-piece,
title = "Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering",
author = "Lee, Wonjin and
Kim, Kyumin and
Lee, Sungjae and
Lee, Jihun and
Kim, Kwang In",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1460/",
pages = "31672--31688",
ISBN = "979-8-89176-390-6",
abstract = "Applying language models (LMs) to tables is challenging due to the mismatch between the two-dimensional structure of tables and the one-dimensional inputs expected by LMs. This mismatch forces linearization, making LMs particularly sensitive to irrelevant cells. Subtable selection mitigates this challenge by isolating question-relevant content prior to answer generation. However, existing approaches either rely on independent row or column selection, failing to capture cross-row and cross-column dependencies, or attempt global reasoning and face challenges similar to holistic table QA under noisy contexts. We propose *PieTa* (Piece of Table), a divide-and-conquer subtable selection framework that progressively aggregates locally selected evidence without requiring explicit global reasoning. *PieTa* uses an iterative, window-based multi-resolution process to construct compact subtables that capture global dependencies while limiting LM exposure to irrelevant content. Extensive experiments demonstrate that *PieTa* consistently outperforms prior subtable-based and holistic table QA approaches."
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<abstract>Applying language models (LMs) to tables is challenging due to the mismatch between the two-dimensional structure of tables and the one-dimensional inputs expected by LMs. This mismatch forces linearization, making LMs particularly sensitive to irrelevant cells. Subtable selection mitigates this challenge by isolating question-relevant content prior to answer generation. However, existing approaches either rely on independent row or column selection, failing to capture cross-row and cross-column dependencies, or attempt global reasoning and face challenges similar to holistic table QA under noisy contexts. We propose *PieTa* (Piece of Table), a divide-and-conquer subtable selection framework that progressively aggregates locally selected evidence without requiring explicit global reasoning. *PieTa* uses an iterative, window-based multi-resolution process to construct compact subtables that capture global dependencies while limiting LM exposure to irrelevant content. Extensive experiments demonstrate that *PieTa* consistently outperforms prior subtable-based and holistic table QA approaches.</abstract>
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%0 Conference Proceedings
%T Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering
%A Lee, Wonjin
%A Kim, Kyumin
%A Lee, Sungjae
%A Lee, Jihun
%A Kim, Kwang In
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F lee-etal-2026-piece
%X Applying language models (LMs) to tables is challenging due to the mismatch between the two-dimensional structure of tables and the one-dimensional inputs expected by LMs. This mismatch forces linearization, making LMs particularly sensitive to irrelevant cells. Subtable selection mitigates this challenge by isolating question-relevant content prior to answer generation. However, existing approaches either rely on independent row or column selection, failing to capture cross-row and cross-column dependencies, or attempt global reasoning and face challenges similar to holistic table QA under noisy contexts. We propose *PieTa* (Piece of Table), a divide-and-conquer subtable selection framework that progressively aggregates locally selected evidence without requiring explicit global reasoning. *PieTa* uses an iterative, window-based multi-resolution process to construct compact subtables that capture global dependencies while limiting LM exposure to irrelevant content. Extensive experiments demonstrate that *PieTa* consistently outperforms prior subtable-based and holistic table QA approaches.
%U https://aclanthology.org/2026.acl-long.1460/
%P 31672-31688
Markdown (Informal)
[Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering](https://aclanthology.org/2026.acl-long.1460/) (Lee et al., ACL 2026)
ACL