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Edinburgh's Syntax-Based Machine Translation Systems., , and . WMT@ACL, page 170-176. The Association for Computer Linguistics, (2013)Edinburgh's Syntax-Based Systems at WMT 2015., , , , and . WMT@EMNLP, page 199-209. The Association for Computer Linguistics, (2015)Syntax-aware Neural Machine Translation Using CCG., , , , , , and . CoRR, (2017)Enabling Robust Grammatical Error Correction in New Domains: Datasets, Metrics, and Analyses., , and . Trans. Assoc. Comput. Linguistics, (2019)Edinburgh's Syntax-Based Systems at WMT 2014., , , , , and . WMT@ACL, page 207-214. The Association for Computer Linguistics, (2014)A baseline revisited: Pushing the limits of multi-segment models for context-aware translation., , , , and . CoRR, (2022)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)Predicting Target Language CCG Supertags Improves Neural Machine Translation., , , , , , and . WMT, page 68-79. Association for Computational Linguistics, (2017)Sockeye 3: Fast Neural Machine Translation with PyTorch., , , , , , , , , and 4 other author(s). CoRR, (2022)