MULTITAT: Benchmarking Multilingual Table-and-Text Question Answering

• URL: http://arxiv.org/abs/2502.17253v1
• Date: Mon, 24 Feb 2025 15:34:09 GMT
• Title: MULTITAT: Benchmarking Multilingual Table-and-Text Question Answering
• Authors: Xuanliang Zhang, Dingzirui Wang, Keyan Xu, Qingfu Zhu, Wanxiang Che,
• Abstract summary: Existing TATQA datasets are limited to English.<n>They overlook the challenges of multilingual TAT-QA.<n>They do not reflect real-world scenarios where tables and texts frequently appear in non-English languages.
• Score: 44.89146464166763
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Question answering on the hybrid context of tables and text (TATQA) is a critical task, with broad applications in data-intensive domains. However, existing TATQA datasets are limited to English, leading to several drawbacks: (i) They overlook the challenges of multilingual TAT-QA and cannot assess model performance in the multilingual setting. (ii) They do not reflect real-world scenarios where tables and texts frequently appear in non-English languages. To address the limitations, we propose the first multilingual TATQA dataset (MULTITAT). Specifically, we sample data from 3 mainstream TATQA datasets and translate it into 10 diverse languages. To align the model TATQA capabilities in English with other languages, we develop a baseline, Ours. Experimental results reveal that the performance on non-English data in MULTITAT drops by an average of 19.4% compared to English, proving the necessity of MULTITAT. We further analyze the reasons for this performance gap. Furthermore, Ours outperforms other baselines by an average of 3.3, demonstrating its effectiveness.

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