1. Modeling ASR Ambiguity for Dialogue State Tracking Using Word Confusion Networks
- Author
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Pal, Vaishali, Guillot, Fabien, Shrivastava, Manish, Renders, Jean-Michel, and Besacier, Laurent
- Subjects
Computer Science - Computation and Language ,Computer Science - Machine Learning ,Computer Science - Sound ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
Spoken dialogue systems typically use a list of top-N ASR hypotheses for inferring the semantic meaning and tracking the state of the dialogue. However ASR graphs, such as confusion networks (confnets), provide a compact representation of a richer hypothesis space than a top-N ASR list. In this paper, we study the benefits of using confusion networks with a state-of-the-art neural dialogue state tracker (DST). We encode the 2-dimensional confnet into a 1-dimensional sequence of embeddings using an attentional confusion network encoder which can be used with any DST system. Our confnet encoder is plugged into the state-of-the-art 'Global-locally Self-Attentive Dialogue State Tacker' (GLAD) model for DST and obtains significant improvements in both accuracy and inference time compared to using top-N ASR hypotheses., Comment: Accepted at Interspeech-2020
- Published
- 2020
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