On-line adaptation of semantic models for spoken language understanding

Spoken language understanding (SLU) systems extract semantic information from speech signals, which is usually mapped onto concept sequences. The distribution of concepts in dialogues are usually sparse. Therefore, general models may fail to model the concept distribution for a dialogue and semantic models can benefit from adaptation. In this paper, we present an instance-based approach for on-line adaptation of semantic models. We show that we can improve the performance of an SLU system on an utterance, by retrieving relevant instances from the training data and using them for on-line adapting the semantic models. The instance-based adaptation scheme uses two different similarity metrics edit distance and n-gram match score on three different to-kenizations; word-concept pairs, words, and concepts. We have achieved a significant improvement (6% relative) in the understanding performance by conducting rescoring experiments on the n-best lists that the SLU outputs. We have also applied a two-level adaptation scheme, where adaptation is first applied to the automatic speech recognizer (ASR) and then to the SLU.