2019
EMNLP
EMNLP 2019
Don’t Just Scratch the Surface: Enhancing Word Representations for Korean with Hanja
Abstract
AbstractWe propose a simple yet effective approach for improving Korean word representations using additional linguistic annotation (i.e. Hanja). We employ cross-lingual transfer learning in training word representations by leveraging the fact that Hanja is closely related to Chinese. We evaluate the intrinsic quality of representations learned through our approach using the word analogy and similarity tests. In addition, we demonstrate their effectiveness on several downstream tasks, including a novel Korean news headline generation task.
🌉
Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
🐝
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 > Learning Paradigms > Transfer Learning
Natural Language Processing > Resources & Methods > Multilingual NLP
Natural Language Processing > Resources & Methods > Text Representation
Deep Learning > Learning Types > Representation Learning
Deep Learning > Learning Types > Transfer Learning
Machine Learning > Learning Types > Multi-Lingual Learning