Spoken dialogue systems typically use predefined semantic slots to parse users' natural language inputs into unified semantic representations. To define the slots, domain experts and professional annotators are often involved, and the cost can be expensive. In this paper, we ask the following question: given a collection of unlabeled raw audios, can we use the frame semantics theory to automatically induce and fill the semantic slots in an unsupervised fashion? To do this, we propose the use of a state-of-the-art frame-semantic parser, and a spectral clustering based slot ranking model that adapts the generic output of the parser to the target semantic space. Empirical experiments on a real-world spoken dialogue dataset show that the automatically induced semantic slots are in line with the reference slots created by domain experts: we observe a mean averaged precision of 69.36% using ASR-transcribed data. Our slot filling evaluations also indicate the promising future of this proposed approach.
To induce and fill the semantic slot in an unsupervised way, Chen et al. REF used a frame-semantic parser to parse the unlabeled ASRtranscribed data and employed a spectral clustering-based slot ranking model to align the output of the parser to the target semantic space.
Unsupervised induction and filling of semantic slots for spoken dialogue systems using frame-semantic parsing
2013 IEEE Workshop on Automatic Speech Recognition and Understanding
2013 IEEE Workshop on Automatic Speech Recognition and Understanding
Computer Science