  Deep reading models for question-answering have demonstrated promising
performance over the last couple of years. However current systems tend to
learn how to cleverly extract a span of the source document, based on its
similarity with the question, instead of seeking for the appropriate answer.
Indeed, a reading machine should be able to detect relevant passages in a
document regarding a question, but more importantly, it should be able to
reason over the important pieces of the document in order to produce an answer
when it is required. To motivate this purpose, we present ReviewQA, a
question-answering dataset based on hotel reviews. The questions of this
dataset are linked to a set of relational understanding competencies that we
expect a model to master. Indeed, each question comes with an associated type
that characterizes the required competency. With this framework, it is possible
to benchmark the main families of models and to get an overview of what are the
strengths and the weaknesses of a given model on the set of tasks evaluated in
this dataset. Our corpus contains more than 500.000 questions in natural
language over 100.000 hotel reviews. Our setup is projective, the answer of a
question does not need to be extracted from a document, like in most of the
recent datasets, but selected among a set of candidates that contains all the
possible answers to the questions of the dataset. Finally, we present several
baselines over this dataset.
