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    <abstract>Hybrid Question-Answering (HQA), which targets reasoning over tables and passages linked from table cells, has witnessed significant research in recent years. A common challenge in HQA and other passage-table QA datasets is that it is generally unrealistic to iterate over all table rows, columns, and linked passages to retrieve evidence. Such a challenge made it difficult for previous studies to show their reasoning ability in retrieving answers. To bridge this gap, we propose a novel Table-alignment-based Cell-selection and Reasoning model (TACR) for hybrid text and table QA, evaluated on the HybridQA and WikiTableQuestions datasets. In evidence retrieval, we design a table-question-alignment enhanced cell-selection method to retrieve fine-grained evidence. In answer reasoning, we incorporate a QA module that treats the row containing selected cells as context. Experimental results over the HybridQA and WikiTableQuestions (WTQ) datasets show that TACR achieves state-of-the-art results on cell selection and outperforms fine-grained evidence retrieval baselines on HybridQA, while achieving competitive performance on WTQ. We also conducted a detailed analysis to demonstrate that being able to align questions to tables in the cell-selection stage can result in important gains from experiments of over 90% table row and column selection accuracy, meanwhile also improving output explainability.</abstract>
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%0 Conference Proceedings
%T TACR: A Table Alignment-based Cell Selection Method for HybridQA
%A Wu, Jian
%A Xu, Yicheng
%A Gao, Yan
%A Lou, Jian-Guang
%A Karlsson, Börje
%A Okumura, Manabu
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F wu-etal-2023-tacr
%X Hybrid Question-Answering (HQA), which targets reasoning over tables and passages linked from table cells, has witnessed significant research in recent years. A common challenge in HQA and other passage-table QA datasets is that it is generally unrealistic to iterate over all table rows, columns, and linked passages to retrieve evidence. Such a challenge made it difficult for previous studies to show their reasoning ability in retrieving answers. To bridge this gap, we propose a novel Table-alignment-based Cell-selection and Reasoning model (TACR) for hybrid text and table QA, evaluated on the HybridQA and WikiTableQuestions datasets. In evidence retrieval, we design a table-question-alignment enhanced cell-selection method to retrieve fine-grained evidence. In answer reasoning, we incorporate a QA module that treats the row containing selected cells as context. Experimental results over the HybridQA and WikiTableQuestions (WTQ) datasets show that TACR achieves state-of-the-art results on cell selection and outperforms fine-grained evidence retrieval baselines on HybridQA, while achieving competitive performance on WTQ. We also conducted a detailed analysis to demonstrate that being able to align questions to tables in the cell-selection stage can result in important gains from experiments of over 90% table row and column selection accuracy, meanwhile also improving output explainability.
%R 10.18653/v1/2023.findings-acl.409
%U https://aclanthology.org/2023.findings-acl.409
%U https://doi.org/10.18653/v1/2023.findings-acl.409
%P 6535-6549

Markdown (Informal)

[TACR: A Table Alignment-based Cell Selection Method for HybridQA](https://aclanthology.org/2023.findings-acl.409) (Wu et al., Findings 2023)

• TACR: A Table Alignment-based Cell Selection Method for HybridQA (Wu et al., Findings 2023)

ACL

• Jian Wu, Yicheng Xu, Yan Gao, Jian-Guang Lou, Börje Karlsson, and Manabu Okumura. 2023. TACR: A Table Alignment-based Cell Selection Method for HybridQA. In Findings of the Association for Computational Linguistics: ACL 2023, pages 6535–6549, Toronto, Canada. Association for Computational Linguistics.