An Enhanced Convolutional Neural Network Model for Answer Selection

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Conference Proceeding
Proceeding WWW '17 Companion Proceedings of the 26th International Conference on World Wide Web Companion, 2017, pp. 789 - 790
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Answer selection is an important task in question answering (QA) from the Web. To address the intrinsic difficulty in encoding sentences with semantic meanings, we introduce a general framework, i.e., Lexical Semantic Feature based Skip Convolution Neural Network (LSF-SCNN), with several op- timization strategies. The intuitive idea is that the granular representations with more semantic features of sentences are deliberately designed and estimated to capture the similar- ity between question-answer pairwise sentences. The experi- mental results demonstrate the effectiveness of the proposed strategies and our model outperforms the state-of-the-art ones by up to 3.5% on the metrics of MAP and MRR.
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