@inproceedings{shu-etal-2026-m3tqa,
    title = "{M}3{TQA}: Massively Multilingual Multitask Table Question Answering",
    author = "Shu, Daixin  and
      Yang, Jian  and
      Wu, Zhenhe  and
      Wu, Xianjie  and
      Cheng, Xianfu  and
      Xiangyuan, Guan  and
      Wang, Yanghai  and
      Wu, Pengfei  and
      Yang, Tingyang  and
      Zhu, Hualei  and
      Zhang, Wei  and
      Zhang, Ge  and
      Liu, Jiaheng  and
      Li, Zhoujun",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1134/",
    pages = "22578--22602",
    ISBN = "979-8-89176-395-1",
    abstract = "Tabular data is a fundamental component of real-world information systems. However, existing multilingual table benchmarks suffer from geolinguistic imbalance - overrepresenting certain languages and lacking sufficient scale for rigorous cross-lingual analysis. To address these limitations, we introduce M3TQA, which is a comprehensive framework for massively multilingual multitask table question answering, including subsequent datasets M3TQA-BENCH and M3TQA-INSTRUCT, featuring tables expanded to 97 languages from Chinese and English sources. M3TQA-BENCH includes 6,606 professionally annotated question-answering pairs across four tasks designed to evaluate nuanced table reasoning capabilities. Additionally, we synthesized the training set M3TQA-INSTRUCT in 97 languages using Large Language Model (LLM). Experiments on state-of-the-art LLMs reveal critical insights into cross-lingual generalization, demonstrating that synthetically generated, unannotated training data can significantly boost performance, particularly for low-resource languages. M3TQA establishes a new standard for multilingual table understanding, providing both a challenging evaluation platform and a scalable methodology for future research."
}