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2023 EMNLP EMNLP 2023

PRHLT’s Submission to WLAC 2023

Abstract

AbstractThis paper describes our submission to the Word-Level AutoCompletion shared task of WMT23. We participated in the English–German and German–English categories. We extended our last year segment-based interactive machine translation approach to address its weakness when no context is available. Additionally, we fine-tune the pre-trained mT5 large language model to be used for autocompletion.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing
🐝 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

Angel Navarro , Miguel Domingo , Francisco Casacuberta

Topics

Deep Learning > Techniques > Pretraining Natural Language Processing > Generation > Text Generation Natural Language Processing > Applications > Machine Translation Machine Learning > Learning Types > Transfer Learning

Keywords

language model pre-trained language model interactive machine translation word-level auto-completion
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