论文标题
终身学习对话系统：聊天机器人在工作中自学
Lifelong Learning Dialogue Systems: Chatbots that Self-Learn On the Job
论文作者
论文摘要
对话系统（也称为聊天机器人）现在已在广泛的应用中使用。但是，他们仍然有一些主要的弱点。一个关键的弱点是，它们通常是通过手动标记的数据和/或用手工制作的规则编写的，他们的知识库（KBS）也由人类专家汇编。由于涉及大量的手动努力，它们很难扩展，并且倾向于对他们理解自然语言的能力有限和KBS中有限的知识产生许多错误。因此，用户满意度通常很低。在本文中，我们建议通过赋予系统不断学习的能力来极大地改善这种情况（1）新世界知识，（2）新的语言表达方式，以将它们扎根于行动，以及（3）新的对话技能，在对话或“工作”中自己“在工作中”，以便随着系统的越来越多地与用户聊天，他们变得越来越多地聊天，他们变得越来越多地知识，并且可以更好地了解与自然的表达和自然的表现，并且可以进行自然的表现，并且可以进行多样的对话，并且能够进行多样的对话，并能够进行多样化的对话，并且能够更好地对话。实现这些目标的一种关键方法是通过通过动词和非动物含量与用户的互动来利用此类系统的多用户环境来自学。本文不仅讨论了在对话期间向用户学习的关键挑战和有希望的方向，还讨论了如何确保学习知识的正确性。
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.
