Dialogue systems, also called chatbots, are now used in a wide range of applications. However, they still have some major weaknesses. One key weakness is that they are typically trained from manually-labeled data and/or written with handcrafted rules, and their knowledge bases (KBs) are also compiled by human experts. Due to the huge amount of manual effort involved, they are difficult to scale and also tend to produce many errors ought to their limited ability to understand natural language and the limited knowledge in their KBs. Thus, the level of user satisfactory is often low. In this paper, we propose to dramatically improve this situation by endowing the system the ability to continually learn (1) new world knowledge, (2) new language expressions to ground them to actions, and (3) new conversational skills, during conversation or "on the job" by themselves so that as the systems chat more and more with users, they become more and more knowledgeable and are better and better able to understand diverse natural language expressions and improve their conversational skills. A key approach to achieving these is to exploit the multi-user environment of such systems to self-learn through interactions with users via verb and non-verb means. The paper discusses not only key challenges and promising directions to learn from users during conversation but also how to ensure the correctness of the learned knowledge.

中文翻译：

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终身学习对话系统：在工作中自学的聊天机器人

对话系统，也称为聊天机器人，现在被用于广泛的应用中。然而，他们仍然有一些主要的弱点。一个关键的弱点是，它们通常是根据手动标记的数据进行训练和/或使用手工规则编写的，并且它们的知识库 (KB) 也是由人类专家编译的。由于涉及大量的手动工作，它们难以扩展，并且由于它们理解自然语言的能力有限且知识库中的知识有限，因此往往会产生许多错误。因此，用户的满意程度通常较低。在本文中，我们建议通过赋予系统持续学习 (1) 新世界知识，(2) 新语言表达以将其用于行动，以及 (3) 新的会话技能，在对话或” 实现这些目标的一个关键方法是利用此类系统的多用户环境，通过动词和非动词方式与用户交互进行自我学习。本文不仅讨论了在对话过程中向用户学习的关键挑战和有希望的方向，而且还讨论了如何确保所学知识的正确性。实现这些目标的一个关键方法是利用此类系统的多用户环境，通过动词和非动词方式与用户交互进行自我学习。本文不仅讨论了在对话过程中向用户学习的关键挑战和有希望的方向，而且还讨论了如何确保所学知识的正确性。