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Generic resources are what you need: Style transfer tasks without task-specific parallel training data., , и . EMNLP (1), стр. 4241-4254. Association for Computational Linguistics, (2021)Extending Feature Decay Algorithms Using Alignment Entropy., , и . FETLT, том 10341 из Lecture Notes in Computer Science, стр. 170-182. Springer, (2016)Evaluation of Natural Language Tools for Italian: EVALITA 2007., , , , , , , , , и 2 other автор(ы). LREC, European Language Resources Association, (2008)An Automatically Built Named Entity Lexicon for Arabic., , , , и . LREC, European Language Resources Association, (2010)Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translation., , , и . WMT, стр. 113-123. Association for Computational Linguistics, (2018)The Importance of Context in Very Low Resource Language Modeling., , и . ICON, стр. 86-92. NLP Association of India (NLPAI), (2021)Fine-grained Human Evaluation of Transformer and Recurrent Approaches to Neural Machine Translation for English-to-Chinese., и . EAMT, стр. 125-134. European Association for Machine Translation, (2020)Low-Resource Unsupervised NMT: Diagnosing the Problem and Providing a Linguistically Motivated Solution., , и . EAMT, стр. 81-90. European Association for Machine Translation, (2020)Abu-MaTran: Automatic building of Machine Translation., , , , , , , , , и 1 other автор(ы). EAMT, European Association for Machine Translation, (2015)CREAMT: Creativity and narrative engagement of literary texts translated by translators and NMT., и . EAMT, стр. 355-356. European Association for Machine Translation, (2022)