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Cheat Codes to Quantify Missing Source Information in Neural Machine Translation., and . NAACL-HLT, page 2472-2477. Association for Computational Linguistics, (2022)Sparse Communication for Distributed Gradient Descent., and . EMNLP, page 440-445. Association for Computational Linguistics, (2017)Language Model Rest Costs and Space-Efficient Storage., , and . EMNLP-CoNLL, page 1169-1178. ACL, (2012)Marian: Fast Neural Machine Translation in C++., , , , , , , , , and 2 other author(s). ACL (4), page 116-121. Association for Computational Linguistics, (2018)The Sockeye 2 Neural Machine Translation Toolkit at AMTA 2020., , , , , and . AMTA, page 110-115. Association for Machine Translation in the Americas, (2020)Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca., , , , and . CoRR, (2023)Edinburgh's Submissions to the 2020 Machine Translation Efficiency Task., , , , , , , and . NGT@ACL, page 218-224. Association for Computational Linguistics, (2020)Normalized Log-Linear Interpolation of Backoff Language Models is Efficient., , , and . ACL (1), The Association for Computer Linguistics, (2016)Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca., , , , , and . EACL (Findings), page 1347-1356. Association for Computational Linguistics, (2024)Making Asynchronous Stochastic Gradient Descent Work for Transformers., and . NGT@EMNLP-IJCNLP, page 80-89. Association for Computational Linguistics, (2019)