Abstract
With the growth of the amount of MOOC users and course diversity, it becomes a hard work for a new MOOC user to find a suitable course and gather other information. In this paper, we propose a natural language dialogue based MOOC guider, which helps users to find a preferred course and provide more information of courses according to a user’s requests. Our method is an end-to-end neural network based method and can be trained efficiently using multi-stage training. Experiments show that our method can understand users’ intent well and produce proper response to finish the task.
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Acknowledgment
This work is supported by NSFC under Grant No. 61532001, and MOE-ChinaMobile under Grant No. MCM20170503.
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Li, Y., Zhang, Y. (2018). MOOC Guider: An End-to-End Dialogue System for MOOC Users. In: U, L., Xie, H. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 11268. Springer, Cham. https://doi.org/10.1007/978-3-030-01298-4_23
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