An integrated approach to robust processing of situated spoken dialogue

Spoken dialogue is notoriously hard to process with standard NLP technologies. Natural spoken dialogue is replete with disfluent, partial, elided or ungrammatical utterances, all of which are difficult to accommodate in a dialogue system. Furthermore, speech recognition is known to be a highly error-prone task, especially for complex, open-ended domains. The combination of these two problems -- ill-formed and/or misrecognised speech inputs -- raises a major challenge to the development of robust dialogue systems. We present an integrated approach for addressing these two issues, based on an incremental parser for Combinatory Categorial Grammar. The parser takes word lattices as input and is able to handle ill-formed and misrecognised utterances by selectively relaxing its set of grammatical rules. The choice of the most relevant interpretation is then realised via a discriminative model augmented with contextual information. The approach is fully implemented in a dialogue system for autonomous robots. Evaluation results on a Wizard of Oz test suite demonstrate very significant improvements in accuracy and robustness compared to the baseline.