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UDapter: Typology-based Language Adapters for Multilingual Dependency Parsing and Sequence Labeling., , , and . Comput. Linguistics, 48 (3): 555-592 (2022)Chunk-Based Verb Reordering in VSO Sentences for Arabic-English Statistical Machine Translation., and . WMT@ACL, page 235-243. Association for Computational Linguistics, (2010)InDeep $\times$ NMT: Empowering Human Translators via Interpretable Neural Machine Translation., and . EAMT, page 311-312. European Association for Machine Translation, (2022)Dynamic Data Selection for Neural Machine Translation., , and . EMNLP, page 1400-1410. Association for Computational Linguistics, (2017)Optimal Word Segmentation for Neural Machine Translation into Dravidian Languages., , and . WAT@ACL/IJCNLP, page 181-190. Association for Computational Linguistics, (2021)Data Augmentation for Low-Resource Neural Machine Translation., , and . ACL (2), page 567-573. Association for Computational Linguistics, (2017)The importance of communicative success for simulating the emergence of a Word Order/Case Marking trade-off with Neural Agents., , and . CogSci, cognitivesciencesociety.org, (2023)Dynamically Shaping the Reordering Search Space of Phrase-Based Statistical Machine Translation., and . Trans. Assoc. Comput. Linguistics, (2013)Five Shades of Noise: Analyzing Machine Translation Errors in User-Generated Text., , and . NUT@IJCNLP, page 28-37. Association for Computational Linguistics, (2015)A Simple but Effective Approach to Improve Arabizi-to-English Statistical Machine Translation., , and . NUT@COLING, page 43-50. The COLING 2016 Organizing Committee, (2016)