Deep Chit-Chat: Deep Learning for Chatbots

Wei Wu, Rui Yan
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引用次数: 11

Abstract

The tutorial is based on our long-term research on open domain conversation, rich hands-on experience on development of Microsoft XiaoIce, and our previous tutorials on EMNLP 2018 and the Web Conference 2019. It starts from a summary of recent achievement made by both academia and industry on chatbots, and then performs a thorough and systematic introduction to state-of-the-art methods for open domain conversation modeling including both retrieval-based methods and generation-based methods. In addition to these, the tutorial also covers some new progress on both groups of methods, such as transition from model design to model learning, transition from knowledge agnostic conversation to knowledge aware conversation, and transition from single-modal conversation to multi-modal conversation. The tutorial is ended by some promising future directions such as how to combine non-task-oriented dialogue systems with task-oriented dialogue systems and how to enhance language learning with chatbots.
深度聊天:用于聊天机器人的深度学习
本教程基于我们对开放域会话的长期研究,微软小冰开发的丰富实践经验,以及我们之前关于EMNLP 2018和Web Conference 2019的教程。本文首先总结了学术界和工业界在聊天机器人方面取得的最新成就,然后对开放领域对话建模的最新方法进行了全面而系统的介绍,包括基于检索的方法和基于生成的方法。除此之外,本教程还涵盖了两组方法的一些新进展,例如从模型设计到模型学习的过渡,从知识不可知论对话到知识感知对话的过渡,以及从单模态对话到多模态对话的过渡。教程的最后介绍了一些有前景的未来方向,比如如何将非任务导向的对话系统与任务导向的对话系统结合起来,以及如何利用聊天机器人增强语言学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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