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2015

Matrix Factorization with Knowledge Graph Propagation for Unsupervised Spoken Language Understanding

3 years 6 months ago
Matrix Factorization with Knowledge Graph Propagation for Unsupervised Spoken Language Understanding
Spoken dialogue systems (SDS) typically require a predefined semantic ontology to train a spoken language understanding (SLU) module. In addition to the annotation cost, a key challenge for designing such an ontology is to define a coherent slot set while considering their complex relations. This paper introduces a novel matrix factorization (MF) approach to learn latent feature vectors for utterances and semantic elements without the need of corpus annotations. Specifically, our model learns the semantic slots for a domain-specific SDS in an unsupervised fashion, and carries out semantic parsing using latent MF techniques. To further consider the global semantic structure, such as inter-word and inter-slot relations, we augment the latent MF-based model with a knowledge graph propagation model based on a slot-based semantic graph and a word-based lexical graph. Our experiments show that the proposed MF approaches produce better SLU models that are able to predict semantic slots a...
Yun-Nung Chen, William Yang Wang, Anatole Gershman
Added 13 Apr 2016
Updated 13 Apr 2016
Type Journal
Year 2015
Where ACL
Authors Yun-Nung Chen, William Yang Wang, Anatole Gershman, Alexander I. Rudnicky
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