The Advance of Dialogue System with Neural Network

Abstract

With the rapid reform of artificial intelligence, the dialogue system as an important branch of artificial intelligence has also developed greatly in recent years. However, dialogue systems also face many challenges, and accurate and well-sampled training data in dialogue systems is often not readily available. How to use the relationship extraction model based on meta-learning from a small number of samples to improve the accuracy of the relationship prediction method on few-shot learning training data. In addition, this article also explores methods of data compression, summarizing previous compression methods in different ways and related work. At the same time, the application of data compression in the dialogue chat model is also studied. The comparison before and after data compression is carried out to make the dialogue chat model maintain the original answer quality when the data part is reduced, and how to use knowledge graph and reinforcement learning to improve the answer quality of the dialogue system after data compression.