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Document-level Neural MT: A Systematic Comparison.

, , , , and . EAMT, page 225-234. European Association for Machine Translation, (2020)

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Is Context Helpful for Chat Translation Evaluation?, , , , and . CoRR, (2024)Unbabel-IST at the WMT Chat Translation Shared Task., , , , and . WMT, page 943-948. Association for Computational Linguistics, (2022)Unbabel's Participation in the WMT19 Translation Quality Estimation Shared Task., , , , , , , and . WMT (3), page 78-84. Association for Computational Linguistics, (2019)Neural vs. Phrase-Based Machine Translation in a Multi-Domain Scenario., , , , and . EACL (2), page 280-284. Association for Computational Linguistics, (2017)Evaluation of Terminology Translation in Instance-Based Neural MT Adaptation., , , , and . EAMT, page 169-178. (2018)FBK's Neural Machine Translation Systems for IWSLT 2016., , , , , , , , , and . IWSLT, International Workshop on Spoken Language Translation, (2016)Unbabel's Submission to the WMT2019 APE Shared Task: BERT-Based Encoder-Decoder for Automatic Post-Editing., , , , and . WMT (3), page 118-123. Association for Computational Linguistics, (2019)Continuous Learning from Human Post-Edits for Neural Machine Translation., , , and . Prague Bull. Math. Linguistics, (2017)Online Word Alignment for Online Adaptive Machine Translation., , and . HaCaT@EACL, page 84-92. Association for Computational Linguistics, (2014)WAGS: A Beautiful English-Italian Benchmark Supporting Word Alignment Evaluation on Rare Words., , , and . LREC, European Language Resources Association (ELRA), (2016)