A clustering approach to semantic decoding

This paper presents a novel algorithm for semantic decoding in spoken language understanding systems. Unlike conventional semantic parsers which either use hand-crafted rules or statistical models trained from fully annotated data, the proposed approach uses an unsupervised sentence clustering technique called Y-clustering to automatically select a set of exemplar sentences from a training corpus. These exemplars are combined with simple sentence-level semantic annotations to form templates which are then used for semantic decoding. The performance of this approach was evaluated in the travel domain using the ATIS corpus. Training is fast and cheap, and the results are significantly better than those achieved using HMM-based or stack-based statistical parsers.