Distributional Pragmatics: Learning to Understand Conversation

Supervisor: Professor Matthew Purver

Research group(s): Cognitive Science

To get computers to understand conversational data, we need models of meaning in dialogue. Conventional methods depend on knowing some taxonomy of dialogue act types, and these are hard to define and quite task- and domain-specific. Recent work in distributional semantics and neural networks has shown that we can directly learn semantics (meaning of words and sentences). Can we do the same for pragmatic aspects of meaning in context, and thus build systems which can learn to interpret dialogue?