PCC:一个对单用户建模的个性化对话系统

对话系统是自然语言处理（NLP）领域中一个重要的下游任务，在近几年得到了越来越多的关注，并取得了很大的发展。然而尽管对话领域已经取得了许多优秀的成果，现有的对话模型在拓展个性化方面依然有很大的局限性。为了使对话模型更符合人类的对话方式，拥有更好的个性化建模能力，该文提出一种新的对单个用户建模的个性化模型PCC（a Personalized Chatbot with Convolution mechanism）。在编码端，PCC通过文本卷积神经网络（TextCNN）处理用户历史回复帖子以得到用户兴趣信息；在解码端，使用相似度搜寻用户历史回答中与当前问题最为匹配的回复和用户ID一起指导生成。实验结果证明，该文模型在生成回复的准确性和多样性上均有较大提升，证明了历史回复信息在个性化建模方面的有效性。

PCC: A Personalized Dialogue System with Single User Modeling

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.