2025
AACL
AACL 2025
Findings of the First Patent Claims Translation Task at WAT2025
Abstract
AbstractThis paper presents the results and findings of the first shared task of translating patent claims. We provide training, development, and test data for participants and perform human evaluation of the submitted translations. This time, 2 teams submitted their translation results. Our analysis of the human-annotated translation errors revealed not only general, domain-independent errors but also errors specific to patent translation. We also found that the human annotation itself exhibited some serious issues. In this paper, we report on these findings.
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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, Security & Privacy, Speech & Audio