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...

• Type: Journal
• Year: 2015
• Where: ACL
• Authors: Yun-Nung Chen, William Yang Wang, Anatole Gershman, Alexander I. Rudnicky
• Keywords: ACL 2015, Computational Linguistics