Paper: A Weakly Supervised Learning Approach For Spoken Language Understanding

ACL ID W06-1624
Title A Weakly Supervised Learning Approach For Spoken Language Understanding
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2006
Authors

In this paper, we present a weakly super- vised learning approach for spoken lan- guage understanding in domain-specific dialogue systems. We model the task of spoken language understanding as a suc- cessive classification problem. The first classifier (topic classifier) is used to iden- tify the topic of an input utterance. With the restriction of the recognized target topic, the second classifier (semantic classifier) is trained to extract the corre- sponding slot-value pairs. It is mainly data-driven and requires only minimally annotated corpus for training whilst re- taining the understanding robustness and deepness for spoken language. Most im- portantly, it allows the employment of weakly supervised strategies for training the two classifiers. We first apply the training strategy of ...