Teaching Machines to Converse

• URL: http://arxiv.org/abs/2001.11701v1
• Date: Fri, 31 Jan 2020 08:28:07 GMT
• Title: Teaching Machines to Converse
• Authors: Jiwei Li
• Abstract summary: This dissertation attempts to tackle challenges presented by neural network models in open-domain dialogue generation systems. We develop interactive question-answering dialogue systems by giving the agent the ability to ask questions and training a conversation agent through interactions with humans in an online fashion.
• Score: 24.64148203917298
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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