2021
EMNLP
EMNLP 2021
Findings of the WMT Shared Task on Machine Translation Using Terminologies
Abstract
AbstractLanguage domains that require very careful use of terminology are abundant and reflect a significant part of the translation industry. In this work we introduce a benchmark for evaluating the quality and consistency of terminology translation, focusing on the medical (and COVID-19 specifically) domain for five language pairs: English to French, Chinese, Russian, and Korean, as well as Czech to German. We report the descriptions and results of the participating systems, commenting on the need for further research efforts towards both more adequate handling of terminologies as well as towards a proper formulation and evaluation of the task.
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Interdisciplinary Bridge
— Healthcare & Medicine and Machine Learning and Natural Language Processing
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Keyword Pioneer
— domain-specific machine translation
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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
Machine Learning > Application Areas > Domain Adaptation
Natural Language Processing > Applications > Machine Translation
Healthcare & Medicine > Clinical > Clinical NLP
Healthcare & Medicine > Clinical > Medical Imaging
Machine Learning > Learning Types > Domain Adaptation
Machine Learning > Learning Types > Deep Learning