Paper: Speakers% Intention Prediction Using Statistics of Multi-level Features in a Schedule Management Domain

ACL ID P08-2058
Title Speakers% Intention Prediction Using Statistics of Multi-level Features in a Schedule Management Domain
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008
Authors

Speaker’s intention prediction modules can be widely used as a pre-processor for reducing the search space of an automatic speech re- cognizer. They also can be used as a pre- processor for generating a proper sentence in a dialogue system. We propose a statistical model to predict speakers’ intentions by using multi-level features. Using the multi-level fea- tures (morpheme-level features, discourse- level features, and domain knowledge-level features), the proposed model predicts speak- ers’ intentions that may be implicated in next utterances. In the experiments, the proposed model showed better performances (about 29% higher accuracies) than the previous model. Based on the experiments, we found that the proposed multi-level features are very effective in speaker’s...