United Arab Emirates

Dialog systems are often designed or trained to output human-like responses. However, some responses may be impossible for a machine to truthfully say (e.g. &quot;that movie made me cry&quot;). Highly anthropomorphic responses might make users uncomfortable or implicitly deceive them into thinking they are interacting with a human. 
We collect human ratings on the feasibility of approximately 900 two-turn dialogs sampled from 9 diverse data sources. Ratings are for two hypothetical machine embodiments: a futuristic humanoid robot and a digital assistant. We find that for some data-sources commonly used to train dialog systems, 20-30\% of utterances are not viewed as possible for a machine. Rating is marginally affected by machine embodiment. 
We explore qualitative and quantitative reasons for these ratings. Finally, we build classifiers and explore how modeling configuration might affect output permissibly, and discuss implications for building less falsely anthropomorphic dialog systems.

EMNLP 2022

Robots-Dont-Cry: Understanding Falsely Anthropomorphic Utterances in Dialog Systems

anthropomorphism

deception

safety

chatbots

dataset

Dialog systems are often designed or trained to output human-like responses. However, some responses may be impossible for a machine to truthfully say (e.g. "that movie made me cry"). Highly anthropomorphic responses might make users uncomfortable or implicitly deceive them into thinking they are interacting with a human. 
We collect human ratings on the feasibility of approximately 900 two-turn dialogs sampled from 9 diverse data sources. Ratings are for two hypothetical machine embodiments: a futuristic humanoid robot and a digital assistant. We find that for some data-sources commonly used to train dialog systems, 20-30\% of utterances are not viewed as possible for a machine. Rating is marginally affected by machine embodiment. 
We explore qualitative and quantitative reasons for these ratings. Finally, we build classifiers and explore how modeling configuration might affect output permissibly, and discuss implications for building less falsely anthropomorphic dialog systems.

poster

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

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Robots-Dont-Cry: Understanding Falsely Anthropomorphic Utterances in Dialog Systems

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A Joint Learning Framework for Restaurant Survival Prediction and Explanation

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Robots-Dont-Cry: Understanding Falsely Anthropomorphic Utterances in Dialog Systems

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Next from EMNLP 2022

A Joint Learning Framework for Restaurant Survival Prediction and Explanation

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads