  Question answering forums are rapidly growing in size with no effective
automated ability to refer to and reuse answers already available for previous
posted questions. In this paper, we develop a methodology for finding
semantically related questions. The task is difficult since 1) key pieces of
information are often buried in extraneous details in the question body and 2)
available annotations on similar questions are scarce and fragmented. We design
a recurrent and convolutional model (gated convolution) to effectively map
questions to their semantic representations. The models are pre-trained within
an encoder-decoder framework (from body to title) on the basis of the entire
raw corpus, and fine-tuned discriminatively from limited annotations. Our
evaluation demonstrates that our model yields substantial gains over a standard
IR baseline and various neural network architectures (including CNNs, LSTMs and
GRUs).
