A weakly supervised learning approach for spoken language understanding

Abstract

In this paper, we present a weakly supervised learning approach for spoken language understanding in domain-specific dialogue systems. We model the task of spoken language understanding as a successive classification problem. The first classifier (topic classifier) is used to identify the topic of an input utterance. With the restriction of the recognized target topic, the second classifier (semantic classifier) is trained to extract the corresponding slot-value pairs. It is mainly data-driven and requires only minimally annotated corpus for training whilst retaining the understanding robustness and deepness for spoken language. Most importantly, it allows the employment of weakly supervised strategies for training the two classifiers. We first apply the training strategy of combining active learning and self-training (Tur et al., 2005) for topic classifier. Also, we propose a practical method for bootstrapping the topic-dependent semantic classifiers from a small amount of labeled sentences. Experiments have been conducted in the context of Chinese public transportation information inquiry domain. The experimental results demonstrate the effectiveness of our proposed SLU framework and show the possibility to reduce human labeling efforts significantly. © 2006 Association for Computational Linguistics.