2020
EMNLP
EMNLP 2020
Machine Translation for English–Inuktitut with Segmentation, Data Acquisition and Pre-Training
Abstract
AbstractTranslating to and from low-resource polysynthetic languages present numerous challenges for NMT. We present the results of our systems for the English–Inuktitut language pair for the WMT 2020 translation tasks. We investigated the importance of correct morphological segmentation, whether or not adding data from a related language (Greenlandic) helps, and whether using contextual word embeddings improves translation. While each method showed some promise, the results are mixed.
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Interdisciplinary Bridge
— 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, Speech & Audio
Authors
Topics
Machine Learning > Application Areas > Data Augmentation
Natural Language Processing > Applications > Machine Translation
Natural Language Processing > Resources & Methods > Multilingual NLP
Machine Learning > Learning Paradigms > Transfer Learning
Natural Language Processing > Generation > Machine Translation