Robust Incremental Semantic Resources for Dialogue

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When humans process language, they do so incrementally, understanding and producing sentences on a word-by-word basis. In conversation, we easily switch roles between speaker and hearer mid-sentence, taking turns speaking and listening to show attention, clarify information or add detail when needed, interactively contributing to a shared, emerging picture of what we mean. If we want human-computer dialogue systems to be natural, efficient and easy to use, they must behave as incrementally as humans do: understanding and reacting interactively on a word-by-word basis rather than insisting on fully-formed sentences. We would prefer a system which behaves as in (1) below to the more familiar but annoying (2), or even the more patient but less interactive (3):(1)Usr: I'd like er [pause] . . .Sys: Yes?Usr: a ticket to Paris from, hang on . . . Sys: Paris, France? Usr: right, from London please. Sys: OK, checking for Paris to London.(2)Usr: I'd like er [pause] . . .Sys: I'm sorry, I don't understand. Please state your destination.(3)Usr: I'd like er [pause] . . .Usr: a ticket to Paris from, hang on . . . Usr: from London please. Sys: OK. Do you mean Paris, France?Previous research has developed computational models of dialogue which can behave incrementally, allowing the kind of interaction shown in (1); but they currently rely on hand-written rules or statistical models to relate words to actions and concepts. These lack the ability to express the complex meanings that human language is so good at conveying, and are time-consuming to create for any new system, domain or task. Instead, they need incremental models which deal with semantics, updating some representation of meaning as each word is heard or spoken, and which can be automatically learned from data; but general methods for doing this are currently lacking. This project will bridge this gap, providing a linguistically-based, learnable framework for incremental semantic interpretation and generation, which can be used to improve and extend existing dialogue systems.The project will start from recent work in theoretical linguistics and dialogue modelling which has produced the incremental semantic processing framework Dynamic Syntax (Kempson et al., 2001). This shows promise in modelling complex incremental dialogue, but is currently under-developed from a practical point of view, needing time-consuming expert hand-crafting, and missing a link between action planning and language generation. This project will address these issues. First, we will develop methods for automatically learning Dynamic Syntax grammars from data, allowing other researchers to easily produce and use their own versions in their own systems. Second, we will develop its methods for generating language so that it can be integrated with the way dialogue systems plan their actions on the fly. These new capabilities will be implemented computationally and evaluated on real data. Together, they will then be used to build a demonstration dialogue system which can behave incrementally, and will be packaged into a publicly available toolkit for researchers to develop their own incremental, semantic dialogue systems.