  Every day, thousands of customers post questions on Amazon product pages.
After some time, if they are fortunate, a knowledgeable customer might answer
their question. Observing that many questions can be answered based upon the
available product reviews, we propose the task of review-based QA. Given a
corpus of reviews and a question, the QA system synthesizes an answer. To this
end, we introduce a new dataset and propose a method that combines information
retrieval techniques for selecting relevant reviews (given a question) and
"reading comprehension" models for synthesizing an answer (given a question and
review). Our dataset consists of 923k questions, 3.6M answers and 14M reviews
across 156k products. Building on the well-known Amazon dataset, we collect
additional annotations, marking each question as either answerable or
unanswerable based on the available reviews. A deployed system could first
classify a question as answerable and then attempt to generate an answer.
Notably, unlike many popular QA datasets, here, the questions, passages, and
answers are all extracted from real human interactions. We evaluate numerous
models for answer generation and propose strong baselines, demonstrating the
challenging nature of this new task.
