2018
EMNLP
EMNLP 2018
CUNI System for the WMT18 Multimodal Translation Task
Abstract
AbstractWe present our submission to the WMT18 Multimodal Translation Task. The main feature of our submission is applying a self-attentive network instead of a recurrent neural network. We evaluate two methods of incorporating the visual features in the model: first, we include the image representation as another input to the network; second, we train the model to predict the visual features and use it as an auxiliary objective. For our submission, we acquired both textual and multimodal additional data. Both of the proposed methods yield significant improvements over recurrent networks and self-attentive textual baselines.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— self attention
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Hot Topic Early Bird
— visual feature
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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 > Multimodal Learning
Machine Learning > Application Areas > Domain Adaptation
Deep Learning > Architectures > Transformers
Deep Learning > Models > Generative Models
Natural Language Processing > Applications > Machine Translation
Deep Learning > Models > Transformers
Deep Learning > Learning Types > Multi-Modal Learning