M2QA: Multi-domain Multilingual Question Answering
Abstract: Generalization and robustness to input variation are core desiderata of machine learning research. Language varies along several axes, most importantly, language instance (e.g. French) and domain (e.g. news). While adapting NLP models to new languages within a single domain, or to new domains within a single language, is widely studied, research in joint adaptation is hampered by the lack of evaluation datasets. This prevents the transfer of NLP systems from well-resourced languages and domains to non-dominant language-domain combinations. To address this gap, we introduce M2QA, a multi-domain multilingual question answering benchmark. M2QA includes 13,500 SQuAD 2.0-style question-answer instances in German, Turkish, and Chinese for the domains of product reviews, news, and creative writing. We use M2QA to explore cross-lingual cross-domain performance of fine-tuned models and state-of-the-art LLMs, and investigate modular approaches to domain and language adaptation. We witness 1) considerable performance _variations_ across domain-language combinations within model classes and 2) considerable performance _drops_ between source and target language-domain combinations across all model sizes. We demonstrate that M2QA is far from solved and new methods to effectively transfer both linguistic and domain-specific information are necessary.
Paper Type: Long
Research Area: Resources and Evaluation
Research Area Keywords: Multilingualism and Cross-Lingual NLP, Question Answering, Resources and Evaluation
Contribution Types: Approaches to low-resource settings, Approaches low compute settings-efficiency, Data resources
Languages Studied: English, German, Turkish, Chinese
Submission Number: 995
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