2019
EMNLP
EMNLP 2019
Controlling Text Complexity in Neural Machine Translation
Abstract
AbstractThis work introduces a machine translation task where the output is aimed at audiences of different levels of target language proficiency. We collect a high quality dataset of news articles available in English and Spanish, written for diverse grade levels and propose a method to align segments across comparable bilingual articles. The resulting dataset makes it possible to train multi-task sequence to sequence models that can translate and simplify text jointly. We show that these multi-task models outperform pipeline approaches that translate and simplify text independently.
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Interdisciplinary Bridge
— Deep Learning and Natural Language Processing
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Trend Setter
— Text Simplification
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Keyword Pioneer
— multi-task sequence-to-sequence model
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Hot Topic Early Bird
— text simplification
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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
Natural Language Processing > Generation > Text Generation
Natural Language Processing > Applications > Machine Translation
Natural Language Processing > Generation > Machine Translation
Deep Learning > Learning Types > Multi-Task Learning
Natural Language Processing > Applications > Text Simplification