Contribution tracking: participating in task-oriented dialogue under uncertainty

The contribution of this dissertation is to show how interlocutors in dialogue can reason probabilistically about natural language interpretation, dialogue state (context), and natural language generation in a way that is consistent with three fundamental claims made by mainstream theories of pragmatic reasoning in human-human dialogue: (1) interlocutors track and exploit the evolving context to coordinate their individual contributions; (2) the current context depends on what the previous utterances of both interlocutors have meant (contributed); (3) what a speaker can recognizably mean (contribute) by a specific choice of words depends on the current context. Mainstream pragmatic theories depend on these assumptions to explain how a speaker can make linguistic choices that the hearer will interpret as intended, but these theories do not lend themselves to straightforward probabilistic reasoning. Engineering approaches to building dialogue systems implement straightforward probabilistic reasoning, but sacrifice one or more (sometimes all) of these fundamental aspects of pragmatic theory in order to do so. This dissertation shows how we can achieve the robustness and data-driven methodology enjoyed by engineering approaches while keeping our interlocutors on a sound theoretical footing, and thereby points the way toward a new class of dialogue systems that are empirically driven, that are robust pragmatic reasoners, and that exhibit human-like sensitivity to the ins and outs of language use in context.