2017
EACL
EACL 2017
Latent Variable Dialogue Models and their Diversity
Abstract
AbstractWe present a dialogue generation model that directly captures the variability in possible responses to a given input, which reduces the ‘boring output’ issue of deterministic dialogue models. Experiments show that our model generates more diverse outputs than baseline models, and also generates more consistently acceptable output than sampling from a deterministic encoder-decoder model.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— response diversity
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Hot Topic Early Bird
— dialogue generation
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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
Deep Learning > Models > Generative Models
Natural Language Processing > Generation > Dialogue Systems
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Natural Language Processing > Applications > Dialogue Systems
Machine Learning > Bayesian & Probabilistic > Variational Inference