2022
COLING
COLING 2022
Open-Domain Dialog Evaluation Using Follow-Ups Likelihood
Abstract
AbstractAutomatic evaluation of open-domain dialogs remains an unsolved problem. Existing methods do not correlate strongly with human annotations. In this paper, we present a new automated evaluation method based on the use of follow-ups. We measure the probability that a language model will continue the conversation with a fixed set of follow-ups (e.g. not really relevant here, what are you trying to say?). When compared against twelve existing methods, our new evaluation achieves the highest correlation with human evaluations.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Natural Language Processing
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Keyword Pioneer
— follow-up probability
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Hot Topic Early Bird
— automated evaluation
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Natural Language Processing > Generation > Dialogue Systems
Natural Language Processing > Generation > Language Modeling
Machine Learning > Learning Types > Representation Learning
Artificial Intelligence > Core AI > Large Language Models
Natural Language Processing > Applications > Dialogue Systems
Machine Learning > Learning Types > Evaluation