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While day-to-day questions come with a variety of answer types, the currentquestion-answering (QA) literature has failed to adequately address the answerdiversity of questions. To this end, we present GooAQ, a large-scale datasetwith a variety of answer types. This dataset contains over 5 million questionsand 3 million answers collected from Google. GooAQ questions are collectedsemi-automatically from the Google search engine using its autocompletefeature. This results in naturalistic questions of practical interest that arenonetheless short and expressed using simple language. GooAQ answers are minedfrom Google's responses to our collected questions, specifically from theanswer boxes in the search results. This yields a rich space of answer types,containing both textual answers (short and long) as well as more structuredones such as collections. We benchmarkT5 models on GooAQ and observe that: (a)in line with recent work, LM's strong performance on GooAQ's short-answerquestions heavily benefit from annotated data; however, (b) their quality ingenerating coherent and accurate responses for questions requiring longresponses (such as 'how' and 'why' questions) is less reliant on observingannotated data and mainly supported by their pre-training. We release GooAQ tofacilitate further research on improving QA with diverse response types.

GooAQ: Open Question Answering with Diverse Answer Types