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Neural machine translation with a polysynthetic low resource language.

, , and . Mach. Transl., 34 (4): 325-346 (2020)

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Addressing Distribution Shift at Test Time in Pre-trained Language Models., and . CoRR, (2022)Fuzzy-Match Repair Guided by Quality Estimation., , and . IEEE Trans. Pattern Anal. Mach. Intell., 44 (3): 1264-1277 (2022)Findings of the IWSLT 2023 Evaluation Campaign., , , , , , , , , and 51 other author(s). IWSLT@ACL, page 1-61. Association for Computational Linguistics, (2023)Classification of US Supreme Court Cases Using BERT-Based Techniques., , and . RANLP, page 1207-1215. INCOMA Ltd., Shoumen, Bulgaria, (2023)An Example of (Too Much) Hyper-Parameter Tuning In Suicide Ideation Detection., , , and . ICWSM, page 1158-1162. AAAI Press, (2023)AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages., , , , , , , , , and 7 other author(s). ACL (1), page 6279-6299. Association for Computational Linguistics, (2022)Improving Translations by Combining Fuzzy-Match Repair with Automatic Post-Editing., , , and . MTSummit (1), page 256-266. European Association for Machine Translation, (2019)Findings of the LoResMT 2021 Shared Task on COVID and Sign Language for Low-resource Languages., , , , , and . CoRR, (2021)Fuzzy-match repair using black-box machine translation systems: what can be expected?, , and . AMTA (1), page 27-39. The Association for Machine Translation in the Americas, (2016)Letting a Neural Network Decide Which Machine Translation System to Use for Black-Box Fuzzy-Match Repair., , , and . EAMT, page 229-238. (2018)