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    <abstract>We study politeness phenomena in nine typologically diverse languages. Politeness is an important facet of communication and is sometimes argued to be cultural-specific, yet existing computational linguistic study is limited to English. We create TyDiP, a dataset containing three-way politeness annotations for 500 examples in each language, totaling 4.5K examples. We evaluate how well multilingual models can identify politeness levels – they show a fairly robust zero-shot transfer ability, yet fall short of estimated human accuracy significantly. We further study mapping the English politeness strategy lexicon into nine languages via automatic translation and lexicon induction, analyzing whether each strategy’s impact stays consistent across languages. Lastly, we empirically study the complicated relationship between formality and politeness through transfer experiments. We hope our dataset will support various research questions and applications, from evaluating multilingual models to constructing polite multilingual agents.</abstract>
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%0 Conference Proceedings
%T TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages
%A Srinivasan, Anirudh
%A Choi, Eunsol
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F srinivasan-choi-2022-tydip
%X We study politeness phenomena in nine typologically diverse languages. Politeness is an important facet of communication and is sometimes argued to be cultural-specific, yet existing computational linguistic study is limited to English. We create TyDiP, a dataset containing three-way politeness annotations for 500 examples in each language, totaling 4.5K examples. We evaluate how well multilingual models can identify politeness levels – they show a fairly robust zero-shot transfer ability, yet fall short of estimated human accuracy significantly. We further study mapping the English politeness strategy lexicon into nine languages via automatic translation and lexicon induction, analyzing whether each strategy’s impact stays consistent across languages. Lastly, we empirically study the complicated relationship between formality and politeness through transfer experiments. We hope our dataset will support various research questions and applications, from evaluating multilingual models to constructing polite multilingual agents.
%R 10.18653/v1/2022.findings-emnlp.420
%U https://aclanthology.org/2022.findings-emnlp.420
%U https://doi.org/10.18653/v1/2022.findings-emnlp.420
%P 5723-5738

Markdown (Informal)

[TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages](https://aclanthology.org/2022.findings-emnlp.420) (Srinivasan & Choi, Findings 2022)

• TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages (Srinivasan & Choi, Findings 2022)

ACL

• Anirudh Srinivasan and Eunsol Choi. 2022. TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5723–5738, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.