Abstract
Most of previous methods on question retrieval treat all words as equally important. This paper employs a bidirectional long short-term memory network to predict word salience weight in the question, which is hinted by the word’s matching status in the answer. Our method is trained on a large corpus of natural question-answer pairs, and so it requires no human annotation. We conduct experiments on question retrieval in a cQA dataset. The results show that our model outperforms traditional methods by a wide margin.
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Huang, X., Dai, X. (2018). Learning Term Weight with Long Short-Term Memory for Question Retrieval. In: Hong, JF., Su, Q., Wu, JS. (eds) Chinese Lexical Semantics. CLSW 2018. Lecture Notes in Computer Science(), vol 11173. Springer, Cham. https://doi.org/10.1007/978-3-030-04015-4_53
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DOI: https://doi.org/10.1007/978-3-030-04015-4_53
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