2017
ACL
ACL 2017
Exploring Neural Text Simplification Models
Abstract
AbstractWe present the first attempt at using sequence to sequence neural networks to model text simplification (TS). Unlike the previously proposed automated TS systems, our neural text simplification (NTS) systems are able to simultaneously perform lexical simplification and content reduction. An extensive human evaluation of the output has shown that NTS systems achieve almost perfect grammaticality and meaning preservation of output sentences and higher level of simplification than the state-of-the-art automated TS systems
🌉
Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
🧭
Keyword Pioneer
— content reduction
🐣
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
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Deep Learning > Architectures > Neural Networks
Natural Language Processing > Generation > Text Generation
Machine Learning > Learning Types > Representation Learning
Deep Learning > Learning Types > Deep Learning
Deep Learning > Architectures > Recurrent Neural Networks