2021
EMNLP
EMNLP 2021
Are BERTs Sensitive to Native Interference in L2 Production?
Abstract
AbstractWith the essays part from The International Corpus Network of Asian Learners of English (ICNALE) and the TOEFL11 corpus, we fine-tuned neural language models based on BERT to predict English learners’ native languages. Results showed neural models can learn to represent and detect such native language impacts, but multilingually trained models have no advantage in doing so.
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The Questioner
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Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
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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
Natural Language Processing > Applications > Text Classification
Natural Language Processing > Resources & Methods > Large Language Models
Natural Language Processing > Resources & Methods > Multilingual NLP
Machine Learning > Learning Types > Transfer Learning
Deep Learning > Models > Transformers
Deep Learning > Learning Types > Fine-Tuning
Natural Language Processing > Applications > Natural Language Understanding