Finding similar questions in large-scale community QA forums
Name
965799043-MIT.pdf
Description
Full printable version
Size
2.82 MB
Format
Adobe PDF
Checksum (MD5)
9be340427c967ffda02ecbbb458ecf00
Author(s)
Joshi, Hrishikesh S
Advisor(s)
Regina Barzilay.
Date Issued
2016
Publisher
Massachusetts Institute of Technology
Abstract
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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