Denoising Table-Text Retrieval for Open-Domain Question Answering

• URL: http://arxiv.org/abs/2403.17611v1
• Date: Tue, 26 Mar 2024 11:44:49 GMT
• Title: Denoising Table-Text Retrieval for Open-Domain Question Answering
• Authors: Deokhyung Kang, Baikjin Jung, Yunsu Kim, Gary Geunbae Lee,
• Abstract summary: In table-text open-domain question answering, a retriever system retrieves relevant evidence from tables and text to answer questions. Previous studies have two common challenges: firstly, their retrievers can be affected by false-positive labels in training datasets. We propose Denoised Table-Text Retriever (DoTTeR) to overcome these issues.
• Score: 6.711626456283439
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In table-text open-domain question answering, a retriever system retrieves relevant evidence from tables and text to answer questions. Previous studies in table-text open-domain question answering have two common challenges: firstly, their retrievers can be affected by false-positive labels in training datasets; secondly, they may struggle to provide appropriate evidence for questions that require reasoning across the table. To address these issues, we propose Denoised Table-Text Retriever (DoTTeR). Our approach involves utilizing a denoised training dataset with fewer false positive labels by discarding instances with lower question-relevance scores measured through a false positive detection model. Subsequently, we integrate table-level ranking information into the retriever to assist in finding evidence for questions that demand reasoning across the table. To encode this ranking information, we fine-tune a rank-aware column encoder to identify minimum and maximum values within a column. Experimental results demonstrate that DoTTeR significantly outperforms strong baselines on both retrieval recall and downstream QA tasks. Our code is available at https://github.com/deokhk/DoTTeR.

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