M-TBQA: Multimodal Table-Based Question Answering
Authors: 
Jingwen Zeng, Zhidong Wu, Rongrong Zheng, Wenting Xue, Chenhui Wang, Xiaoyang Yu, Tao Zhang, Shaozu Yuan, Tiangang ZhuAuthors Info & Claims
Jingwen Zeng, Zhidong Wu, Rongrong Zheng, Wenting Xue, Chenhui Wang, Xiaoyang Yu, Tao Zhang, Shaozu Yuan, Tiangang ZhuAuthors Info & ClaimsPages 227 - 231
Abstract
In recent years, there has been considerable interest in the research on table-based question answering (TBQA). These studies aim to understand the table content and generate the answer for the given table. With the rapid growth of the Internet, table content is no longer confined to pure text but also includes images. However, most previous methods overlook the important visual information linked to table cells and focus solely on parsing questions to logic form using textual tabular data. This limitation prevents the exploration of the multimodal application of TBQA. Therefore, we propose a novel task multimodal table-based question answering (M-TBQA), which is required to perform multimodal question answering on tabular data with images. Specially, M-TBQA initially identifies candidate rows relevant to the question using a table-relevance net, and then predicts the expected answer using an answer prediction net. This approach effectively harnesses multimodal information from both tabular data and images to predict answers accurately. Additionally, it highlights the significant role of images in the M-TBQA task. To facilitate related research in the further, the dataset will be released at https://github.com/cooperResearch001/M-TBQA/tree/main
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Index Terms
- M-TBQA: Multimodal Table-Based Question Answering
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Published In
October 2023
1065 pages
ISBN:9798400709449
DOI:10.1145/3650215
Copyright © 2023 ACM.
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Published: 16 April 2024
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ICMLCA 2023
ICMLCA 2023: 2023 4th International Conference on Machine Learning and Computer Application
October 27 - 29, 2023
Hangzhou, China
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