  Confidently making progress on multilingual modeling requires challenging,
trustworthy evaluations. We present TyDi QA---a question answering dataset
covering 11 typologically diverse languages with 204K question-answer pairs.
The languages of TyDi QA are diverse with regard to their typology---the set of
linguistic features each language expresses---such that we expect models
performing well on this set to generalize across a large number of the world's
languages. We present a quantitative analysis of the data quality and
example-level qualitative linguistic analyses of observed language phenomena
that would not be found in English-only corpora. To provide a realistic
information-seeking task and avoid priming effects, questions are written by
people who want to know the answer, but don't know the answer yet, and the data
is collected directly in each language without the use of translation.
