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Imperial College London Submission to VATEX Video Captioning Task., , , , and . CoRR, (2019)Sentence-level confidence estimation for MT., , , , , , , and . SMART@EAMT, European Association for Machine Translation, (2009)Deciding When, How and for Whom to Simplify., , and . ECAI, volume 325 of Frontiers in Artificial Intelligence and Applications, page 2172-2179. IOS Press, (2020)Pangeanic in the EXPERT Project: Exploiting Empirical appRoaches to Translation., , , , and . MTSummit (European projects), (2013)A Post-Editing Dataset in the Legal Domain: Do we Underestimate Neural Machine Translation Quality?, , , , , , , , and . LREC, page 3692-3697. European Language Resources Association, (2020)ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations., , , , , and . ACL, page 4668-4679. Association for Computational Linguistics, (2020)EASSE: Easier Automatic Sentence Simplification Evaluation., , , and . EMNLP/IJCNLP (3), page 49-54. Association for Computational Linguistics, (2019)Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications., , , , , and . EMNLP (1), page 5865-5875. Association for Computational Linguistics, (2021)FIND: Human-in-the-Loop Debugging Deep Text Classifiers., , and . EMNLP (1), page 332-348. Association for Computational Linguistics, (2020)WMDO: Fluency-based Word Mover's Distance for Machine Translation Evaluation., , and . WMT (2), page 494-500. Association for Computational Linguistics, (2019)