  One of the challenges in large-scale information retrieval (IR) is to develop
fine-grained and domain-specific methods to answer natural language questions.
Despite the availability of numerous sources and datasets for answer retrieval,
Question Answering (QA) remains a challenging problem due to the difficulty of
the question understanding and answer extraction tasks. One of the promising
tracks investigated in QA is to map new questions to formerly answered
questions that are `similar'. In this paper, we propose a novel QA approach
based on Recognizing Question Entailment (RQE) and we describe the QA system
and resources that we built and evaluated on real medical questions. First, we
compare machine learning and deep learning methods for RQE using different
kinds of datasets, including textual inference, question similarity and
entailment in both the open and clinical domains. Second, we combine IR models
with the best RQE method to select entailed questions and rank the retrieved
answers. To study the end-to-end QA approach, we built the MedQuAD collection
of 47,457 question-answer pairs from trusted medical sources, that we introduce
and share in the scope of this paper. Following the evaluation process used in
TREC 2017 LiveQA, we find that our approach exceeds the best results of the
medical task with a 29.8% increase over the best official score. The evaluation
results also support the relevance of question entailment for QA and highlight
the effectiveness of combining IR and RQE for future QA efforts. Our findings
also show that relying on a restricted set of reliable answer sources can bring
a substantial improvement in medical QA.
