2021
EMNLP
EMNLP 2021
[RETRACTED] Sequence Mixup for Zero-Shot Cross-Lingual Part-Of-Speech Tagging
Abstract
AbstractThere have been efforts in cross-lingual transfer learning for various tasks. We present an approach utilizing an interpolative data augmentation method, Mixup, to improve the generalizability of models for part-of-speech tagging trained on a source language, improving its performance on unseen target languages. Through experiments on ten languages with diverse structures and language roots, we put forward its applicability for downstream zero-shot cross-lingual tasks.
🌉
Interdisciplinary Bridge
— 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
Machine Learning > Learning Types > Zero-Shot Learning
Machine Learning > Application Areas > Data Augmentation
Natural Language Processing > Understanding > Part-of-Speech Tagging
Machine Learning > Learning Types > Transfer Learning
Machine Learning > Learning Paradigms > Zero-Shot Learning
Deep Learning > Learning Types > Data Augmentation