2023
EMNLP
EMNLP 2023
SJTU-MTLAB’s Submission to the WMT23 Word-Level Auto Completion Task
Abstract
AbstractWord-level auto-completion (WLAC) plays a crucial role in Computer-Assisted Translation. In this paper, we describe the SJTU-MTLAB’s submission to the WMT23 WLAC task. We propose a joint method to incorporate the machine translation task to the WLAC task. The proposed approach is general and can be applied to various encoder-based architectures. Through extensive experiments, we demonstrate that our approach can greatly improve performance, while maintaining significantly small model sizes.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— joint method
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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