Combining Statistical and Knowledge-Based Spoken Language Understanding in Conditional Models

Spoken Language Understanding (SLU) addresses the problem of extracting semantic meaning conveyed in an utterance. The traditional knowledge-based approach to this problem is very expensive -- it requires joint expertise in natural language processing and speech recognition, and best practices in language engineering for every new domain. On the other hand, a statistical learning approach needs a large amount of annotated data for model training, which is seldom available in practical applications outside of large research labs. A generative HMM/CFG composite model, which integrates easy-toobtain domain knowledge into a data-driven statistical learning framework, has previously been introduced to reduce data requirement. The major contribution of this paper is the investigation of integrating prior knowledge and statistical learning in a conditional model framework. We also study and compare conditional random fields (CRFs) with perceptron learning for SLU. Experimental results show t...

• Type: Conference
• Year: 2006
• Where: ACL
• Authors: Ye-Yi Wang, Alex Acero, Milind Mahajan, John Lee
• Keywords: ACL 2006, ACL 2007, Conditional Model Framework, Conditional Models, Statistical Learning