Motivated by psycholinguistic findings that eye gaze is tightly linked to human language production, we developed an unsupervised approach based on translation models to automatically learn the mappings between words and objects on a graphic display during human machine conversation. The experimental results indicate that user eye gaze can provide useful information to establish such mappings, which have important implications in automatically acquiring and interpreting user vocabularies for conversational systems. .
