Semi-supervised Question Retrieval with Gated Convolutions

Understand

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.