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What Works When in Context-aware Neural Machine Translation?

, , and . EAMT, page 147-156. European Association for Machine Translation, (2023)

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Efficient document alignment across scenarios., and . Mach. Transl., 33 (3): 205-237 (2019)Exploiting a Large Strongly Comparable Corpus., , and . LREC, European Language Resources Association (ELRA), (2016)Evaluating Domain Adaptation for Machine Translation Across Scenarios., , , , and . LREC, European Language Resources Association (ELRA), (2018)Lexical Normalization of Spanish Tweets with Rule-Based Components and Language Models., , and . Proces. del Leng. Natural, (2014)The Multilingual Anonymisation Toolkit for Public Administrations (MAPA) Project., , , , , , , , , and 9 other author(s). EAMT, page 471-472. European Association for Machine Translation, (2020)Lexical Normalization of Spanish Tweets with Preprocessing Rules, Domain-specific Edit Distances, and Language Models., , and . Tweet-Norm@SEPLN, volume 1086 of CEUR Workshop Proceedings, page 59-63. CEUR-WS.org, (2013)Making the most of comparable corpora in Neural Machine Translation: a case study., and . Lang. Resour. Evaluation, 56 (3): 943-971 (2022)Statistically-Guided Controlled Language Authoring., , and . CNL, volume 9767 of Lecture Notes in Computer Science, page 37-47. Springer, (2016)Online Learning over Time in Adaptive Neural Machine Translation., , , and . RANLP, page 411-420. INCOMA Ltd., (2021)Set-Theoretic Alignment for Comparable Corpora., and . ACL (1), The Association for Computer Linguistics, (2016)