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| PCC: A Personalized Dialogue System with Single User Modeling |
| GUO Yu2,3, DOU Zhicheng1,3, WEN Jirong3,4 |
1. Gaoling School of Artificial Intelligence, Renmin University of China, Beijing 100086, China;
2. School of Information, Renmin University of China, Beijing 100086, China;
3. Beijing Key Laboratory of Big Data Management and Analysis Method, Beijing 100086, China;
4. Key Laboratory of Data Engineering and Knowledge Engineering, Beijing 100086, China; |
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Abstract Dialogue system is an important downstream task in the field of natural language processing (NLP) receiving more and more attention in recent years. In order to make the dialogue models more in line with the way of human dialogue and have better personalized modeling capabilities, this paper proposes a new personalized model PCC（a Personalized Chatbot with Convolution mechanism）to model a single user. At the encoder, text convolutional neural network (TextCNN) is used to process user history posts to obtain user interest information. At the decoder, we search for the reply that best matches the current question in the users historical answers through similarity, so as to guide the models generation together with user ID. Experimental results show that our model can improve the accuracy and diversity of the generation, and reveal the effectiveness of historical information in personalized modeling.
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Received: 20 October 2020
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