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.
🌉
Interdisciplinary Bridge
- Deep Learning and Natural Language Processing
📈
Trend Setter
- Text Simplification
🧭
Keyword Pioneer
- multi-task sequence-to-sequence model
🐣
Hot Topic Early Bird
- text simplification
🐝
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