Abstract
With the continuous development of the network, Question-Answering system has become a way for people to get information quickly. The QA task aims to provide precise and quick answers to user questions from a collection of documents or a database. In this paper, we introduce an attention based deep learning model to match the question and answer sentence. The proposed model employs a bidirectional long-short term memory(BLSTM) to solve the problem of lack features. And we also use the attention mechanism which allows the question to focus on a certain part of the candidate answer. Finally, we evaluate our model and the results show that our approach outperforms the method of feature construction based on machine learning. And the attention mechanism improves the matching accuracy.
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Project supported by the Basic scientific research projects of colleges and universities in Liaoning Province (2017L317).
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Song, B., Zhuo, Y., Li, X. (2018). Research on Question-Answering System Based on Deep Learning. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10942. Springer, Cham. https://doi.org/10.1007/978-3-319-93818-9_50
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DOI: https://doi.org/10.1007/978-3-319-93818-9_50
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