Abstract
This paper describes our method for sub-task 2 of Task 5: multi-turn conversation retrieval, in NLPCC2018. Given a context and some candidate responses, the task is to choose the most reasonable response for the context. It can be regarded as a matching problem. To address this task, we propose a deep neural model named RCMN which focus on modeling relevance consistency of conversations. In addition, we adopt one existing deep learning model which is advanced for multi-turn response selection. And we propose an ensemble strategy for the two models. Experiments show that RCMN has good performance, and ensemble of two models makes good improvement. The official results show that our solution takes 2nd place. We open the source of our code on GitHub, so that other researchers can reproduce easily.
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Acknowledgments
This work was supported in part by the Natural Science Foundation of China (Grand Nos. U61672081, 1636211, 61370126), and Beijing Advanced Innovation Center for Imaging Technology (No. BAICIT-2016001) and National Key R&D Program of China (No. 2016QY04W0802).
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Wang, Y., Yan, Z., Li, Z., Chao, W. (2018). Response Selection of Multi-turn Conversation with Deep Neural Networks. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11108. Springer, Cham. https://doi.org/10.1007/978-3-319-99495-6_10
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DOI: https://doi.org/10.1007/978-3-319-99495-6_10
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