Paper: Using Machine Learning To Explore Human Multimodal Clarification Strategies

ACL ID P06-2085
Title Using Machine Learning To Explore Human Multimodal Clarification Strategies
Venue Annual Meeting of the Association of Computational Linguistics
Session Poster Session
Year 2006
Authors

We investigate the use of machine learn- ing in combination with feature engineer- ing techniques to explore human multi- modal clarification strategies and the use of those strategies for dialogue systems. We learn from data collected in a Wizard- of-Oz study where different wizards could decide whether to ask a clarification re- quest in a multimodal manner or else use speech alone. We show that there is a uniform strategy across wizards which is based on multiple features in the context. These are generic runtime features which can be implemented in dialogue systems. Our prediction models achieve a weighted f-score of 85.3% (which is a 25.5% im- provement over a one-rule baseline). To assess the effects of models, feature dis- cretisation, and selection, we also conduct a regression ana...