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Crowd-based MT Evaluation for non-English Target Languages.

, , , and . EAMT, page 229-237. European Association for Machine Translation, (2012)

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The Fifth PASCAL Recognizing Textual Entailment Challenge., , , , and . TAC, NIST, (2009)Extending the MuST-C Corpus for a Comparative Evaluation of Speech Translation Technology., , , , , and . EAMT, page 359-360. European Association for Machine Translation, (2022)Assessing the Impact of Translation Errors on Machine Translation Quality with Mixed-effects Models., , , and . EMNLP, page 1643-1653. ACL, (2014)How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation., , , , and . ACL/IJCNLP (Findings), volume ACL/IJCNLP 2021 of Findings of ACL, page 3576-3589. Association for Computational Linguistics, (2021)EnetCollect in Italy., , , , , , , and . CLiC-it, volume 2253 of CEUR Workshop Proceedings, CEUR-WS.org, (2018)Evaluating Cross-Language Annotation Transfer in the MultiSemCor Corpus., , and . COLING, (2004)The IWSLT 2015 Evaluation Campaign., , , , , and . IWSLT (Evaluation Campaign), (2015)Machine Translation for Machines: the Sentiment Classification Use Case., , , and . EMNLP/IJCNLP (1), page 1368-1374. Association for Computational Linguistics, (2019)Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation., , , , and . ACL (1), page 1807-1824. Association for Computational Linguistics, (2022)Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus., , , , , and . ACL, page 6923-6933. Association for Computational Linguistics, (2020)