Go to ACL ARR 2025 February homepageMULTITAT: Benchmarking Multilingual Table-and-Text Question Answering
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.
Paper Type: Long
Research Area: Question Answering
Research Area Keywords: Question Answering
Contribution Types: Data resources
Languages Studied: Bengali, Chinese, English, French, German, Japanese, Russian, Spanish, Swahili, Telugu, Thai
Submission Number: 3475
Loading