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The Devil Is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation.

, , , , , , , , , and . WMT, page 1066-1083. Association for Computational Linguistics, (2023)

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Harnessing Multilinguality in Unsupervised Machine Translation for Rare Languages., , , and . CoRR, (2020)Representation of cognitive processes using the minimum spanning tree of local meshes., , , , and . EMBC, page 6780-6783. IEEE, (2013)Controlling Computation versus Quality for Neural Sequence Models., , and . CoRR, (2020)PaLM 2 Technical Report., , , , , , , , , and 43 other author(s). CoRR, (2023)Conditional Random Fields for Land Use/Land Cover Classification and Complex Region Detection., , and . SSPR/SPR, volume 7626 of Lecture Notes in Computer Science, page 216-224. Springer, (2012)Adaptive Scheduling for Multi-Task Learning., , and . CoRR, (2019)Theano: A Python framework for fast computation of mathematical expressions, , , , , , , , , and 103 other author(s). (2016)cite arxiv:1605.02688Comment: 19 pages, 5 figures.Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets., , , , , , , , , and 42 other author(s). Trans. Assoc. Comput. Linguistics, (2022)A Loss Curvature Perspective on Training Instability in Deep Learning., , , , , , , , and . CoRR, (2021)Bilex Rx: Lexical Data Augmentation for Massively Multilingual Machine Translation., , , and . CoRR, (2023)