论文标题
从知识增强到多任务：到类似人类的对话系统
From Knowledge Augmentation to Multi-tasking: Towards Human-like Dialogue Systems
论文作者
论文摘要
自从人工智能初期以来，建立可以与人类交谈的对话代理的目标一直是研究人员的长期梦想。著名的图灵测试提议判断人工智能代理人对其与人类对话的不可区分性的最终有效性。人级对话系统的建设非常具有挑战性，这不足为奇。但是，尽管基于规则的系统的早期努力发现了有限的成功，但深度学习的出现使这一主题可以取得巨大的进步。 在本文中，我们专注于解决人工对话剂与人级对话者之间差距的众多问题的方法。提出了这些方法，并以受一般最新AI方法的启发的方式进行了实验。但是他们还针对对话系统所具有的特征。
The goal of building dialogue agents that can converse with humans naturally has been a long-standing dream of researchers since the early days of artificial intelligence. The well-known Turing Test proposed to judge the ultimate validity of an artificial intelligence agent on the indistinguishability of its dialogues from humans'. It should come as no surprise that human-level dialogue systems are very challenging to build. But, while early effort on rule-based systems found limited success, the emergence of deep learning enabled great advance on this topic. In this thesis, we focus on methods that address the numerous issues that have been imposing the gap between artificial conversational agents and human-level interlocutors. These methods were proposed and experimented with in ways that were inspired by general state-of-the-art AI methodologies. But they also targeted the characteristics that dialogue systems possess.
