Ninth International Conference on Spoken Language Processing

Pittsburgh, PA, USA
September 17-21, 2006

Robust Interpretation in Dialogue by Combining Confidence Scores with Contextual Features

Matthew Purver (1), Florin Ratiu (1), Lawrence Cavedon (2)

(1) Stanford University, USA; (2) National ICT Australia, Australia

We present an approach to dialogue management and interpretation that evaluates and selects amongst candidate dialogue moves based on features at multiple levels. Multiple interpretation methods can be combined, multiple speech recognition and parsing hypotheses tested, and multiple candidate dialogue moves considered to choose the highest scoring hypothesis overall. We integrate hypotheses generated from shallow slot-filling methods and from relatively deep parsing, using pragmatic information. We show that this gives more robust performance than using either approach alone, allowing n-best list reordering to correct errors in speech recognition or parsing.

Full Paper

Bibliographic reference.  Purver, Matthew / Ratiu, Florin / Cavedon, Lawrence (2006): "Robust interpretation in dialogue by combining confidence scores with contextual features", In INTERSPEECH-2006, paper 1314-Mon1A1O.1.