TACR: A Table-alignment-based Cell-selection and Reasoning Model for Hybrid Question-Answering

• URL: http://arxiv.org/abs/2305.14682v1
• Date: Wed, 24 May 2023 03:42:44 GMT
• Title: TACR: A Table-alignment-based Cell-selection and Reasoning Model for Hybrid Question-Answering
• Authors: Jian Wu, Yicheng Xu, Yan Gao, Jian-Guang Lou, B\"orje F. Karlsson, Manabu Okumura
• Abstract summary: We propose a Table-alignment-based Cell-selection and Reasoning model (TACR) for hybrid text and table QA. 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.
• Score: 31.79113994947629
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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