Finding similar questions in large-scale community QA forums
Author(s)
Joshi, Hrishikesh S![Thumbnail](/bitstream/handle/1721.1/106118/965799043-MIT.pdf.jpg?sequence=3&isAllowed=y)
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Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Regina Barzilay.
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Question answering forums are rapidly growing in size with no automated ability to refer to and reuse existing answers. 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 are scarce and fragmented, driven largely by participants. We design a novel combination of recurrent and convolutional models (gated convolutions) 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 a 10% gain over a standard IR baseline, and a 6% gain over standard neural network architectures (including CNNs and LSTMs) trained analogously.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016. Cataloged from PDF version of thesis. Includes bibliographical references (pages 47-51).
Date issued
2016Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer SciencePublisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.