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We study politeness phenomena in nine typologically diverse languages.Politeness is an important facet of communication and is sometimes argued to becultural-specific, yet existing computational linguistic study is limited toEnglish. We create TyDiP, a dataset containing three-way politeness annotationsfor 500 examples in each language, totaling 4.5K examples. We evaluate how wellmultilingual models can identify politeness levels -- they show a fairly robustzero-shot transfer ability, yet fall short of estimated human accuracysignificantly. We further study mapping the English politeness strategy lexiconinto nine languages via automatic translation and lexicon induction, analyzingwhether each strategy's impact stays consistent across languages. Lastly, weempirically study the complicated relationship between formality and politenessthrough transfer experiments. We hope our dataset will support various researchquestions and applications, from evaluating multilingual models to constructingpolite multilingual agents.

TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages