  Question retrieval is a crucial subtask for community question answering.
Previous research focus on supervised models which depend heavily on training
data and manual feature engineering. In this paper, we propose a novel
unsupervised framework, namely reduced attentive matching network (RAMN), to
compute semantic matching between two questions. Our RAMN integrates together
the deep semantic representations, the shallow lexical mismatching information
and the initial rank produced by an external search engine. For the first time,
we propose attention autoencoders to generate semantic representations of
questions. In addition, we employ lexical mismatching to capture surface
matching between two questions, which is derived from the importance of each
word in a question. We conduct experiments on the open CQA datasets of
SemEval-2016 and SemEval-2017. The experimental results show that our
unsupervised model obtains comparable performance with the state-of-the-art
supervised methods in SemEval-2016 Task 3, and outperforms the best system in
SemEval-2017 Task 3 by a wide margin.
