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TMU NMT System with Japanese BART for the Patent task of WAT 2021., и . WAT@ACL/IJCNLP, стр. 133-137. Association for Computational Linguistics, (2021)Can Monolingual Pre-trained Encoder-Decoder Improve NMT for Distant Language Pairs?, и . PACLIC, стр. 235-243. Association for Computational Lingustics, (2021)Enhancing Few-shot Cross-lingual Transfer with Target Language Peculiar Examples., и . ACL (Findings), стр. 747-767. Association for Computational Linguistics, (2023)Does Masked Language Model Pre-training with Artificial Data Improve Low-resource Neural Machine Translation?, , , и . EACL (Findings), стр. 2171-2180. Association for Computational Linguistics, (2023)Zero-shot North Korean to English Neural Machine Translation by Character Tokenization and Phoneme Decomposition., , и . ACL (student), стр. 72-78. Association for Computational Linguistics, (2020)Pruning Multilingual Large Language Models for Multilingual Inference., , , и . CoRR, (2024)Learning How to Translate North Korean through South Korean., , , и . LREC, стр. 6711-6718. European Language Resources Association, (2022)Korean-to-Japanese Neural Machine Translation System using Hanja Information., , и . WAT@AAC/IJCNLPL, стр. 127-134. Association for Computational Linguistics, (2020)North Korean Neural Machine Translation through South Korean Resources., , , , и . ACM Trans. Asian Low Resour. Lang. Inf. Process., 22 (9): 223:1-223:22 (сентября 2023)Simultaneous Domain Adaptation of Tokenization and Machine Translation., , , , и . PACLIC, стр. 36-45. Association for Computational Linguistics, (2023)