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    <abstract>Indirectness is a common feature of daily communication, yet is underexplored in NLP research for both low-resource as well as high-resource languages. Indirect Question Answering (IQA) aims at classifying the polarity of indirect answers. In this paper, we present two multilingual corpora for IQA of varying quality that both cover English, Standard German and Bavarian, a German dialect without standard orthography: InQA+, a small high-quality evaluation dataset with hand-annotated labels, and GenIQA, a larger training dataset, that contains artificial data generated by GPT-4o-mini. We find that IQA is a pragmatically hard task that comes with various challenges, based on several experiment variations with multilingual transformer models (mBERT, XLM-R and mDeBERTa). We suggest and employ recommendations to tackle these challenges. Our results reveal low performance, even for English, and severe overfitting. We analyse various factors that influence these results, including label ambiguity, label set and dataset size. We find that the IQA performance is poor in high- (English, German) and low-resource languages (Bavarian) and that it is beneficial to have a large amount of training data. Further, GPT-4o-mini does not possess enough pragmatic understanding to generate high-quality IQA data in any of our tested languages.</abstract>
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%0 Conference Proceedings
%T Indirect Question Answering in English, German and Bavarian: A Challenging Task for High- and Low-Resource Languages Alike
%A Winkler, Miriam
%A Blaschke, Verena
%A Plank, Barbara
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F winkler-etal-2026-indirect
%X Indirectness is a common feature of daily communication, yet is underexplored in NLP research for both low-resource as well as high-resource languages. Indirect Question Answering (IQA) aims at classifying the polarity of indirect answers. In this paper, we present two multilingual corpora for IQA of varying quality that both cover English, Standard German and Bavarian, a German dialect without standard orthography: InQA+, a small high-quality evaluation dataset with hand-annotated labels, and GenIQA, a larger training dataset, that contains artificial data generated by GPT-4o-mini. We find that IQA is a pragmatically hard task that comes with various challenges, based on several experiment variations with multilingual transformer models (mBERT, XLM-R and mDeBERTa). We suggest and employ recommendations to tackle these challenges. Our results reveal low performance, even for English, and severe overfitting. We analyse various factors that influence these results, including label ambiguity, label set and dataset size. We find that the IQA performance is poor in high- (English, German) and low-resource languages (Bavarian) and that it is beneficial to have a large amount of training data. Further, GPT-4o-mini does not possess enough pragmatic understanding to generate high-quality IQA data in any of our tested languages.
%R 10.63317/3qtihanapruj
%U https://aclanthology.org/2026.lrec-1.336/
%U https://doi.org/10.63317/3qtihanapruj
%P 4268-4289

Markdown (Informal)

[Indirect Question Answering in English, German and Bavarian: A Challenging Task for High- and Low-Resource Languages Alike](https://aclanthology.org/2026.lrec-1.336/) (Winkler et al., LREC 2026)

• Indirect Question Answering in English, German and Bavarian: A Challenging Task for High- and Low-Resource Languages Alike (Winkler et al., LREC 2026)

ACL

• Miriam Winkler, Verena Blaschke, and Barbara Plank. 2026. Indirect Question Answering in English, German and Bavarian: A Challenging Task for High- and Low-Resource Languages Alike. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 4268–4289, Palma de Mallorca, Spain. ELRA Language Resource Association.