  While day-to-day questions come with a variety of answer types, the current
question-answering (QA) literature has failed to adequately address the answer
diversity of questions. To this end, we present GooAQ, a large-scale dataset
with a variety of answer types. This dataset contains over 5 million questions
and 3 million answers collected from Google. GooAQ questions are collected
semi-automatically from the Google search engine using its autocomplete
feature. This results in naturalistic questions of practical interest that are
nonetheless short and expressed using simple language. GooAQ answers are mined
from Google's responses to our collected questions, specifically from the
answer boxes in the search results. This yields a rich space of answer types,
containing both textual answers (short and long) as well as more structured
ones 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-answer
questions heavily benefit from annotated data; however, (b) their quality in
generating coherent and accurate responses for questions requiring long
responses (such as 'how' and 'why' questions) is less reliant on observing
annotated data and mainly supported by their pre-training. We release GooAQ to
facilitate further research on improving QA with diverse response types.
