@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://aclanthology.org/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."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="shu-etal-2026-m3tqa">
<titleInfo>
<title>M3TQA: Massively Multilingual Multitask Table Question Answering</title>
</titleInfo>
<name type="personal">
<namePart type="given">Daixin</namePart>
<namePart type="family">Shu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jian</namePart>
<namePart type="family">Yang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhenhe</namePart>
<namePart type="family">Wu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xianjie</namePart>
<namePart type="family">Wu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xianfu</namePart>
<namePart type="family">Cheng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Guan</namePart>
<namePart type="family">Xiangyuan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yanghai</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Pengfei</namePart>
<namePart type="family">Wu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tingyang</namePart>
<namePart type="family">Yang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hualei</namePart>
<namePart type="family">Zhu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wei</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ge</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiaheng</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhoujun</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: ACL 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="family">Liakata</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Viviane</namePart>
<namePart type="given">P</namePart>
<namePart type="family">Moreira</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiajun</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Jurgens</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, United States</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-395-1</identifier>
</relatedItem>
<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.</abstract>
<identifier type="citekey">shu-etal-2026-m3tqa</identifier>
<location>
<url>https://aclanthology.org/2026.findings-acl.1134/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>22578</start>
<end>22602</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T M3TQA: Massively Multilingual Multitask Table Question Answering
%A Shu, Daixin
%A Yang, Jian
%A Wu, Zhenhe
%A Wu, Xianjie
%A Cheng, Xianfu
%A Xiangyuan, Guan
%A Wang, Yanghai
%A Wu, Pengfei
%A Yang, Tingyang
%A Zhu, Hualei
%A Zhang, Wei
%A Zhang, Ge
%A Liu, Jiaheng
%A Li, Zhoujun
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F shu-etal-2026-m3tqa
%X 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.
%U https://aclanthology.org/2026.findings-acl.1134/
%P 22578-22602
Markdown (Informal)
[M3TQA: Massively Multilingual Multitask Table Question Answering](https://aclanthology.org/2026.findings-acl.1134/) (Shu et al., Findings 2026)
ACL
- Daixin Shu, Jian Yang, Zhenhe Wu, Xianjie Wu, Xianfu Cheng, Guan Xiangyuan, Yanghai Wang, Pengfei Wu, Tingyang Yang, Hualei Zhu, Wei Zhang, Ge Zhang, Jiaheng Liu, and Zhoujun Li. 2026. M3TQA: Massively Multilingual Multitask Table Question Answering. In Findings of the Association for Computational Linguistics: ACL 2026, pages 22578–22602, San Diego, California, United States. Association for Computational Linguistics.