← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-long.1460 · 记录 #1
Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:53
摘要
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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记录 #1 · 2026-07 · 最新观测版本
Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering
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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