Diverse Conversation Generation System with Sentence Function Classification

Abstract

This paper mainly studies the implementation of diverse conversation generation system based on end-to-end neural networks and sentence classification on sentence function. In existing work, the output of the conversational system is mostly in the form of one type of sentence, for instance, declarative sentence. The type of sentence function is not well distributed. There is still insufficient diversity in the output of conversational system, which could be unattractive to users. A good conversational system could well interact with users by generating diverse output, including asking and responding, driving conversations to go further. Generating different type of output sentence is necessary to conversational systems, which is also challenging. Therefore, this paper introduces the idea of diverse conversation into the generative system, and designs the Diverse Conversation Generation (DCG) model. The model adopts a sentence function classifier trained independently to supervise the model output with modified loss function and back-propagation. The DCG model increases the diversity of output sentence, which could guide user to chat more with the system, extend the quality of conversation, and improve the user experience. The model is experimented on two different sequence-to sequence models, evaluated with perplexity and classify entropy, achieves better performance compared with two base models.