2019
ACL
ACL 2019
Dialogue Natural Language Inference
Abstract
AbstractConsistency is a long standing issue faced by dialogue models. In this paper, we frame the consistency of dialogue agents as natural language inference (NLI) and create a new natural language inference dataset called Dialogue NLI. We propose a method which demonstrates that a model trained on Dialogue NLI can be used to improve the consistency of a dialogue model, and evaluate the method with human evaluation and with automatic metrics on a suite of evaluation sets designed to measure a dialogue model’s consistency.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Natural Language Processing
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Keyword Pioneer
— consistency 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, Security & Privacy, Speech & Audio
Authors
Topics
Natural Language Processing > Generation > Dialogue Systems
Natural Language Processing > Resources & Methods > Natural Language Inference
Natural Language Processing > Applications > Natural Language Inference
Natural Language Processing > Applications > Dialogue Systems
Natural Language Processing > Understanding > Natural Language Inference
Deep Learning > Learning Types > Classification
Artificial Intelligence > Core AI > Dialogue Systems