Papers with Dialog systems

1 papers
Robots-Dont-Cry: Understanding Falsely Anthropomorphic Utterances in Dialog Systems (2022.emnlp-main)

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Challenge: Dialog systems often output human-like responses, but some are impossible for a machine to say.
Approach: They collect ratings on the feasibility of 900 two-turn dialogs from 9 data sources . they build classifiers and explore how modeling configuration might affect output permissibly .
Outcome: The proposed model can be used to train human-like dialogs, but it is not anthropomorphic.

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