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Findings of the IWSLT 2023 Evaluation Campaign., , , , , , , , , and 51 other author(s). IWSLT@ACL, page 1-61. Association for Computational Linguistics, (2023)CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality., , , , , and . NAACL-HLT (Findings), page 616-632. Association for Computational Linguistics, (2022)Dynamic adjustment of language models for automatic speech recognition using word similarity., , and . SLT, page 426-432. IEEE, (2016)The University of Edinburgh's Neural MT Systems for WMT17., , , , , , , and . WMT, page 389-399. Association for Computational Linguistics, (2017)Fine-Tuned Machine Translation Metrics Struggle in Unseen Domains., , , , , and . CoRR, (2024)Faithful Target Attribute Prediction in Neural Machine Translation., , , and . CoRR, (2021)Distilling Multiple Domains for Neural Machine Translation., , and . EMNLP (1), page 4500-4511. Association for Computational Linguistics, (2020)Pseudo-label Training and Model Inertia in Neural Machine Translation., , , , and . ICLR, OpenReview.net, (2023)Zero-Resource Neural Machine Translation with Monolingual Pivot Data., and . NGT@EMNLP-IJCNLP, page 99-107. Association for Computational Linguistics, (2019)Sockeye 3: Fast Neural Machine Translation with PyTorch., , , , , , , , , and 4 other author(s). CoRR, (2022)