  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.
