Jiahui Guo
ABSTRACT
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 optimization strategies. The intuitive idea is that the granular representations with more semantic features of sentences are deliberately designed and estimated to capture the similarity between question-answer pairwise sentences. The experimental 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.
- A. Severyn and A. Moschitti. Learning to rank short text pairs with convolutional deep neural networks. In SIGIR, pages 373--382, 2015. Google Scholar
Digital Library
- A. Severyn and A. Moschitti. Modeling relational information in question-answer pairs with convolutional neural networks. arXiv:1604.01178, 2016.Google Scholar
- M. Wang, N. A. Smith, and T. Mitamura. What is the jeopardy model? a quasi-synchronous grammar for qa. In EMNLP, pages 22--32, 2007.Google Scholar
- Z. Wang, H. Mi, and A. Ittycheriah. Sentence similarity learning by lexical decomposition and composition. In COLING, 2016.Google Scholar
- Y. Yang, W. tau Yih, and C. Meek. Wikiqa: A challenge dataset for open-domain question answering. In EMNLP, pages 2013--2018, 2015.Google ScholarCross Ref
- W. Yin and H. Schütze. Convolutional neural network for paraphrase identification. In NAACL, 2015. Google ScholarCross Ref
- L. Yu, K. M. Hermann, P. Blunsom, and S. Pulman. Deep learning for answer sentence selection. In NIPS Deep Learning Workshop, 2014.Google Scholar
Index Terms
- An Enhanced Convolutional Neural Network Model for Answer Selection
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