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Findings of the 2020 Conference on Machine Translation (WMT20), , , , , , , , , and 11 other author(s). Proceedings of the Fifth Conference on Machine Translation, page 1--55. Online, Association for Computational Linguistics, (November 2020)Zero-Shot Cross-Lingual Dependency Parsing through Contextual Embedding Transformation., and . CoRR, (2021)Pixel Representations for Multilingual Translation and Data-efficient Cross-lingual Transfer., , , and . CoRR, (2023)Alternative Input Signals Ease Transfer in Multilingual Machine Translation., , , , , , and . CoRR, (2021)Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation., , , and . CoRR, (2021)Embedding-Enhanced Giza++: Improving Alignment in Low- and High- Resource Scenarios Using Embedding Space Geometry., , and . CoRR, (2021)On the Evaluation of Machine Translation for Terminology Consistency., , , , , , and . CoRR, (2021)Multilingual Representation Distillation with Contrastive Learning., , , and . CoRR, (2022)Introduction to the Special Issue on Machine Translation of Asian Languages., and . ACM Trans. Asian Lang. Inf. Process., 8 (2): 5:1-5:2 (2009)Syntax-Based Statistical Machine Translation Philip Williams, Rico Sennrich, Matt Post, Philipp Koehn (University of Edinburgh, University of Edinburgh, Johns Hopkins University, Johns Hopkins University), edited by Graeme Hirst, volume 33), 2016, xvii+190 pp; paperback, ISBN 978-1-62705-900-8; ebook, ISBN 978-1-62705-502-4; doi: 10.2200/S00716ED1V04Y201604HLT033, $70., , , , , and . Computational Linguistics, (2017)