Personality dialogue agent based on personality description and conversation history

In the study of dialogue system, personalized dialogue mainly focuses on the semantic matching degree between response and role. However, various factors, such as semantic style, dialogue noise, and sparse personality information, can affect the performance of the model. For this purpose, we construct a novel personalized dialogue model. Based on the personality description information and conversation history, it uses the information enhancement algorithm to cluster the personality description text into a number of fine sparse categories, and uses the feature classifier to precisely select the features highly relevant to the current dialogue situation according to the input query content. At the same time, the history information selector will fine-filter the conversation history, retaining the parts that are valuable for generating replies. We combine the processed personality description text with the filtered historical context information, and send it to the decoder through the feature cue learning strategy for deep processing to generate personalized responses. Our model integrates the proposed algorithms, classifiers, selectors and feature cue learning strategies to build a complete dialogue system. Experiments on two datasets show that our model is superior to other models in terms of consistency and coherence.