Leveraging frame semantics and distributional semantics for unsupervised semantic slot induction in spoken dialogue systems

Distributional semantics and frame semantics are two representative views on language understanding in the statistical world and the linguistic world, respectively. In this paper, we combine the best of two worlds to automatically induce the semantic slots for spoken dialogue systems. Given a collection of unlabeled audio files, we exploit continuous-valued word embeddings to augment a probabilistic frame-semantic parser that identifies key semantic slots in an unsupervised fashion. In experiments, our results on a real-world spoken dialogue dataset show that the distributional word representations significantly improve the adaptation of FrameNet-style parses of ASR decodings to the target semantic space; that comparing to a state-of-the-art baseline, a 13% relative average precision improvement is achieved by leveraging word vectors trained on two 100-billion words datasets; and that the proposed technology can be used to reduce the costs for designing task-oriented spoken dialogue systems.