Incorporating temporal and semantic information with eye gaze for automatic word acquisition in multimodal conversational systems

Abstract

One major bottleneck in conversational systems is their incapability in interpreting unexpected user language inputs such as out-ofvocabulary words. To overcome this problem, conversational systems must be able to learn new words automatically during human machine conversation. Motivated by psycholinguistic findings on eye gaze and human language processing, we are developing techniques to incorporate human eye gaze for automatic word acquisition in multimodal conversational systems. This paper investigates the use of temporal alignment between speech and eye gaze and the use of domain knowledge in word acquisition. Our experiment results indicate that eye gaze provides a potential channel for automatically acquiring new words. The use of extra temporal and domain knowledge can significantly improve acquisition performance. © 2008 Association for Computational Linguistics.