Motivated by psycholinguistic findings, we are currently investigating the role of eye gaze in spoken language understanding for multimodal conversational systems. Our assumption is that, during human machine conversation, a user’s eye gaze on the graphical display indicates salient entities on which the user’s attention is focused. The specific domain information about the salient entities is likely to be the content of communication and therefore can be used to constrain speech hypotheses and help language understanding. Based on this assumption, this paper describes an exploratory study that incorporates eye gaze in salience modeling for spoken language processing. Our empirical results show that eye gaze has a potential in improving automated language processing. Eye gaze is subconscious and involuntary during human machine conversation. Our work motivates more in-depth investigation on eye gaze in attention prediction and its implication in automated language processing.
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