2024
EMNLP
EMNLP 2024
TSU HITS’s Submissions to the WMT 2024 General Machine Translation Shared Task
Abstract
AbstractThis paper describes the TSU HITS team’s submission system for the WMT’24 general translation task. We focused on exploring the capabilities of discrete diffusion models for the English-to-{Russian, German, Czech, Spanish} translation tasks in the constrained track. Our submission system consists of a set of discrete diffusion models for each language pair. The main advance is using a separate length regression model to determine the length of the output sequence more precisely.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— length regression
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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