面向端到端的情感对话生成研究综述

Survey of Research on End-to-End Emotional Dialogue Generation

人机对话作为人工智能的重要研究内容,受到了学术界和工业界的广泛关注。受到深度学习在自然语言处理成功应用的启发,越来越多的神经网络模型被研究者关注。其中基于端到端的神经网络模型能够从大规模语料中学习到有价值的规律和特征,生成有意义且多样性的回复,被广泛地应用于情感对话生成研究中。面向基于端到端模型的情感对话生成研究展开综述。首先,针对现有的研究成果,梳理并介绍了当前情感对话生成研究面向的任务和主要解决的问题,并且做出了详细的定义,整理并介绍了情感对话生成模型建模所需的数据集。其次,对端到端的神经网络模型的原理进行了简单的概述,并且分析和总结了情感对话生成研究在每个基础模型中的改进、研究现状、模型涉及的评价指标以及模型的性能。再次,对现阶段涉及到的模型评价方式按照自动评价以及人工评价方式进行了总结。最后,对未来情感对话生成研究的发展方向进行了展望。

Human-machine dialogue,an important research component of artificial intelligence,has received widespread attention from academia and industry.Inspired by the successful application of deep learning in natural language processing,a growing number of neural network models are being focused on by researchers.Among them,end-to-end based neural network models are able to learn valuable patterns and features from large-scale corpus to generate meaningful and diverse responses,and are widely used in research on emotional dialogue generation.This paper presents a review of the research on end-to-end models for emotional dialogue generation.Firstly,the tasks and main problems addressed by current research on emotional dialogue generation are outlined and defined in detail in the light of existing research results.The datasets required for modeling emotional dialogue generation models are organized and presented.Secondly,a brief overview of the principles of end-to-end neural network models is given,and the improvements in each of the underlying models,the current state of research,the evaluation metrics involved in the models,and the performance of the models are analyzed and summarized.Thirdly,the evaluation methods involved in the current stage of model evaluation are summarized in terms of automatic evaluation as well as manual evaluation.Finally,this paper prospects the development direction of the research on the generation of emotional dialogue in the future.