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Neural Dialogue Model with Retrieval Attention for Personalized Response Generation

机译：具有检索注意力的神经对话模型用于个性化响应生成

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摘要

With the success of new speech-based human-computer interfaces, there is a great need for effective and friendly dialogue agents that can communicate with people naturally and continuously. However, the lack of personality and consistency is one of critical problems in neural dialogue systems. In this paper, we aim to generate consistent response with fixed profile and background information for building a realistic dialogue system. Based on the encoder-decoder model, we propose a retrieval mechanism to deliver natural and fluent response with proper information from a profile database. Moreover, in order to improve the efficiency of training the dataset related to profile information, we adopt a method of pre-training and adjustment for general dataset and profile dataset. Our model is trained by social dialogue data from Weibo. According to both automatic and human evaluation metrics, the proposed model significantly outperforms standard encoder-decoder model and other improved models on providing the correct profile and high-quality responses.
机译：随着基于语音的新型人机界面的成功，人们迫切需要能够与人们自然而连续地进行交流的有效而友好的对话代理。然而，缺乏个性和一致性是神经对话系统中的关键问题之一。在本文中，我们旨在生成固定配置文件和背景信息的一致响应，以构建现实的对话系统。基于编码器-解码器模型，我们提出了一种检索机制，可利用配置文件数据库中的适当信息来传递自然流畅的响应。此外，为了提高训练与轮廓信息有关的数据集的效率，我们采用了一种对一般数据集和轮廓数据集进行预训练和调整的方法。我们的模型由来自微博的社交对话数据训练而成。根据自动和人工评估指标，在提供正确的配置文件和高质量的响应时，建议的模型明显优于标准的编码器-解码器模型和其他改进的模型。

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  • 来源
    《Computers, Materials & Continua》 |2020年第1期|113-122|共10页
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  • 作者单位

    School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing 100083 China Beijing Key Laboratory of Knowledge Engineering for Materials Science University of Science and Technology Beijing Beijing 100083 China;

    Beijing Key Laboratory of Knowledge Engineering for Materials Science University of Science and Technology Beijing Beijing 100083 China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083 China;

    Amphenol AssembleTech Houston TX 77070 US;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Dialogue system; LSTM; encoder-decoder model; attention mechanism;

    机译：对话系统;LSTM;编码器-解码器模型;注意机制;

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