2016
COLING
COLING 2016
Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network
Abstract
AbstractIn this study, we applied a deep LSTM structure to classify dialogue acts (DAs) in open-domain conversations. We found that the word embeddings parameters, dropout regularization, decay rate and number of layers are the parameters that have the largest effect on the final system accuracy. Using the findings of these experiments, we trained a deep LSTM network that outperforms the state-of-the-art on the Switchboard corpus by 3.11%, and MRDA by 2.2%.
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Topic Pioneer
— Deep Learning
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Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
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Trend Setter
— Intent Classification
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Keyword Pioneer
— domain-independent conversation
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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