2018
EMNLP
EMNLP 2018
PD3: Better Low-Resource Cross-Lingual Transfer By Combining Direct Transfer and Annotation Projection
Abstract
AbstractWe consider unsupervised cross-lingual transfer on two tasks, viz., sentence-level argumentation mining and standard POS tagging. We combine direct transfer using bilingual embeddings with annotation projection, which projects labels across unlabeled parallel data. We do so by either merging respective source and target language datasets or alternatively by using multi-task learning. Our combination strategy considerably improves upon both direct transfer and projection with few available parallel sentences, the most realistic scenario for many low-resource target languages.
🌉
Interdisciplinary Bridge
— Artificial Intelligence 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
Machine Learning > Learning Types > Unsupervised Learning
Natural Language Processing > Applications > Machine Translation
Natural Language Processing > Resources & Methods > Multilingual NLP
Machine Learning > Learning Paradigms > Transfer Learning
Machine Learning > Learning Types > Multi-Task Learning
Machine Learning > Learning Types > Transfer Learning