论文标题
教学机
Teaching Machines to Converse
论文作者
论文摘要
长期以来，机器与人类通信的能力与AI的一般成功有关。这可以追溯到艾伦·图灵（Alan Turing）在1950年代初期的划时代作品，这表明，可以通过机器的智慧来测试机器的智力，这台机器可以使人类欺骗人类，因为它可以通过对话对话来相信机器是人类。许多系统从最小的一组著名的规则或手工编码的规则或模板上学习了生成规则，因此既昂贵又难以扩展到开放域情景。最近，神经网络的出现模型解决了对话学习中许多问题无法应对的许多问题：端到端的神经框架提供了可伸缩性和与语言独立的承诺，以及跟踪对话状态，然后以常规系统不可能以一种方式跟踪对话状态，然后在状态和对话行动之间进行映射和对话操作的能力。另一方面，神经系统带来了新的挑战：它们倾向于产生沉闷而通用的反应；他们缺乏一致或连贯的角色；它们通常通过单转交谈来优化，并且无法处理对话的长期成功；他们无法利用与人类的互动。本论文试图应对这些挑战：贡献是两个方面：（1）我们解决了开放域对话生成系统中神经网络模型提出的新挑战； （2）我们通过（a）通过（a）使经纪人能够提出问题以及（b）通过以在线方式与人互动来培训对话代理的能力来开发交互式问题的对话系统，在这种方式中，机器人通过与人类进行交流并从其犯的错误中学习来改善。
The ability of a machine to communicate with humans has long been associated with the general success of AI. This dates back to Alan Turing's epoch-making work in the early 1950s, which proposes that a machine's intelligence can be tested by how well it, the machine, can fool a human into believing that the machine is a human through dialogue conversations. Many systems learn generation rules from a minimal set of authored rules or labels on top of hand-coded rules or templates, and thus are both expensive and difficult to extend to open-domain scenarios. Recently, the emergence of neural network models the potential to solve many of the problems in dialogue learning that earlier systems cannot tackle: the end-to-end neural frameworks offer the promise of scalability and language-independence, together with the ability to track the dialogue state and then mapping between states and dialogue actions in a way not possible with conventional systems. On the other hand, neural systems bring about new challenges: they tend to output dull and generic responses; they lack a consistent or a coherent persona; they are usually optimized through single-turn conversations and are incapable of handling the long-term success of a conversation; and they are not able to take the advantage of the interactions with humans. This dissertation attempts to tackle these challenges: Contributions are two-fold: (1) we address new challenges presented by neural network models in open-domain dialogue generation systems; (2) we develop interactive question-answering dialogue systems by (a) giving the agent the ability to ask questions and (b) training a conversation agent through interactions with humans in an online fashion, where a bot improves through communicating with humans and learning from the mistakes that it makes.
