  As a key task of question answering, question retrieval has attracted much
attention from the communities of academia and industry. Previous solutions
mainly focus on the translation model, topic model, and deep learning
techniques. Distinct from the previous solutions, we propose to construct
fine-grained semantic representations of a question by a learned importance
score assigned to each keyword, so that we can achieve a fine-grained question
matching solution with these semantic representations of different lengths.
Accordingly, we propose a multi-view semantic matching model by reusing the
important keywords in multiple semantic representations.
  As a key of constructing fine-grained semantic representations, we are the
first to use a cross-task weakly supervised extraction model that applies
question-question labelled signals to supervise the keyword extraction process
(i.e. to learn the keyword importance). The extraction model integrates the
deep semantic representation and lexical matching information with statistical
features to estimate the importance of keywords. We conduct extensive
experiments on three public datasets and the experimental results show that our
proposed model significantly outperforms the state-of-the-art solutions.
