Towards efficient human-machine collaboration: Effects of gaze-driven feedback and engagement on performance
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Can listener gaze facilitate goal-oriented human-machine collaboration? In order to jointly solve a task, interlocutors need to establish reference in a shared environment. Our usual mean of communication is natural language but it is not interpreted in isolation. Other modalities (e.g. gaze and gestures) support communicative success in situated interactions. Specifically, an artificial speaker (natural language generation NLG system) can exploit listener gaze to realise an effective interaction strategy by responding to it with verbal feedback. Considering an object identification task for assembly under system instruction, we show that proactive feedback generated on the basis of object inspections can improve task performance. In particular, providing information incrementally in subsequent chunks turns out to be more efficient than' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Human-Computer Interaction","Natural Language Generation","Listener Gaze","Referential Success","Multimodal Systems"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Can listener gaze facilitate goal-oriented human-machine collaboration? In order to jointly solve a task, interlocutors need to establish reference in a shared environment. Our usual mean of communication is natural language but it is not interpreted in isolation. Other modalities (e.g. gaze and gestures) support communicative success in situated interactions. Specifically, an artificial speaker (natural language generation NLG system) can exploit listener gaze to realise an effective interaction strategy by responding to it with verbal feedback. Considering an object identification task for assembly under system instruction, we show that proactive feedback generated on the basis of object inspections can improve task performance. In particular, providing information incrementally in subsequent chunks turns out to be more efficient than' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Can listener gaze facilitate goal-oriented human-machine collaboration? In order to jointly solve a task, interlocutors need to establish reference in a shared environment. Our usual mean of communication is natural language but it is not interpreted in isolation. Other modalities (e.g. gaze and gestures) support communicative success in situated interactions. Specifically, an artificial speaker (natural language generation NLG system) can exploit listener gaze to realise an effective interaction strategy by responding to it with verbal feedback. Considering an object identification task for assembly under system instruction, we show that proactive feedback generated on the basis of object inspections can improve task performance. In particular, providing information incrementally in subsequent chunks turns out to be more efficient than
Human-Computer Interaction; Natural Language Generation; Listener Gaze;  Referential Success;  Multimodal Systems