2017
EMNLP
EMNLP 2017
Interactive Visualization and Manipulation of Attention-based Neural Machine Translation
Abstract
AbstractWhile neural machine translation (NMT) provides high-quality translation, it is still hard to interpret and analyze its behavior. We present an interactive interface for visualizing and intervening behavior of NMT, specifically concentrating on the behavior of beam search mechanism and attention component. The tool (1) visualizes search tree and attention and (2) provides interface to adjust search tree and attention weight (manually or automatically) at real-time. We show the tool gives various methods to understand NMT.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Natural Language Processing
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Hot Topic Early Bird
— beam search
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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
Artificial Intelligence > Core AI > Interpretability
Natural Language Processing > Applications > Machine Translation
Natural Language Processing > Generation > Machine Translation
Deep Learning > Learning Types > Deep Learning
Artificial Intelligence > Core AI > Language
Deep Learning > Techniques > Attention