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Statistical Machine Translation, July 2011, pp. 71—77 // https://www.aclweb.org/anthology/W11-2105/

2090

Banerjee S., Lavie A. (2005). METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments / Proceedings of the ACL 2005 Workshop on Intrinsic and Extrinsic Evaluation Measures for MT and/or Summarization // https://www.aclweb.org/anthology/W05-0909/

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Han A. L.-F. (2017). LEPOR: An Augmented Machine Translation Evaluation Metric // https://arxiv.org/abs/1703.08748

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Han A. L.-F., Wong D. F., Chao L. S., He L., Lu Y. (2014). Unsupervised Quality Estimation Model for English to German Translation and Its Application in Extensive Supervised Evaluation / The Scientific World Journal, Vol. 2014 // https://doi.org/10.1155/2014/760301

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Aaron Li-Feng Han A. L.-F., Wong D. F., Chao L. S., He L., Lu Y., Xing J., Zeng X. (2013). Language-independent Model for Machine Translation Evaluation with Reinforced Factors / Proceedings of the XIV Machine Translation Summit (Nice, September 2–6, 2013), pp. 215—222 // http://www.mt-archive.info/10/MTS-2013-Han.pdf

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Lin C.-Y. (2004). ROUGE: a Package for Automatic Evaluation of Summaries / Proceedings of the Workshop on Text Summarization Branches Out (WAS 2004), Barcelona, Spain, July 25—26, 2004 // https://www.aclweb.org/anthology/W04-1013/

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Zhang T., Kishore V., Wu F., Weinberger K. Q., Artzi Y. (2020). BERTScore: Evaluating Text Generation with BERT // https://arxiv.org/abs/1904.09675

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Marie B. (2022). BLEU: A Misunderstood Metric from Another Age But still used today in AI research / Towards Data Science, Nov 5, 2022. // https://towardsdatascience.com/bleu-a-misunderstood-metric-from-another-age-d434e18f1b37

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Papineni K., Roukos S., Ward T., Zhu W.-J. (2002). BLEU: a Method for Automatic Evaluation of Machine Translation / Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311—317 // https://doi.org/10.3115/1073083.1073135

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Callison-Burch C., Koehn P., Monz C., Post M., Soricut R., Specia L. (2013). Findings of the 2012 Workshop on Statistical Machine Translation / NAACL 2012 Seventh Workshop on Statistical Machine Translation // http://www.statmt.org/wmt12/pdf/WMT02.pdf

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Gao P., He Z., Wu H., Wang H. (2022). Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation // https://arxiv.org/abs/2206.02368

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Wei J., Bosma M., Zhao V. Y., Guu К., Yu A. W., Lester B., Du N., Dai A. M., Le Q. V. (2021). Finetuned Language Models Are Zero-Shot Learners // https://arxiv.org/abs/2109.01652

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Liang X., Wu L., Li J., Wang Y., Meng Q., Qin T., Chen W., Zhang M., Liu T.-Y. (2020). R-Drop: Regularized Dropout for Neural Networks // https://arxiv.org/abs/2106.14448

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Лагунина И., Ольшанская Е. (2004). Машинный перевод / Радио Свобода, 21 января // https://www.svoboda.org/a/24196111.html

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Savenkov K. (2018). State of the machine translation by Intento (2018) // https://www.slideshare.net/KonstantinSavenkov/state-of-the-machine-translation-by-intento-july-2018

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Savenkov K. (2019). State of the Machine Translation (January 2019) / Intento, Mar 9, 2019 // https://blog.inten.to/state-of-the-machine-translation-january-2019-dffe15884d63

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Savenkov K. (2019). State of the Machine Translation (June 2019) // Intento, Jun 20, 2019 // https://blog.inten.to/state-of-the-machine-translation-june-2019-e3ffb457b76c

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The State of Machine Translation 2020. Independent multi-domain evaluation of commercial Machine Translation engines (2020) / Intento // https://try.inten.to/mt_report_2020

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Stanford Human-Centered Artificial Intelligence (HAI) (2019). Artificial Intelligence Index Report 2019 // https://hai.stanford.edu/sites/default/files/ai_index_2019_report.pdf

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Manning C. D., Raghavan P., Schütze H. (2008). Introduction to Information Retrieval. Cambridge University Press // https://books.google.ru/books?id=t1PoSh4uwVcC

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Bengio Y., Ducharme R., Vincent P., Jauvin C. (2003). A Neural Probabilistic Language Model / Journal of Machine Learning Research, Vol. 3 (2003), pp. 1137—1155 // http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf

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Wikipedia contributors. (2021, August 2). Size of Wikipedia. In Wikipedia, The Free Encyclopedia. Retrieved 08:00, August 2, 2021, from https://en.wikipedia.org/wiki/Wikipedia:Size_of_Wikipedia

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BNC Consortium (2001). The British National Corpus, version 2 (BNC World) // http://www.natcorp.ox.ac.uk/

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* Корпусная лингвистика — раздел языкознания, занимающийся разработкой, созданием и использованием текстовых корпусов.

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Wallis S. (2016). Why Chomsky was Wrong About Corpus Linguistics / corp.ling.stats: statistics for corpus linguists, November 2, 2016 // https://corplingstats.wordpress.com/2016/11/02/why-chomsky-was-wrong/

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Firth J. R. (1957). A synopsis of linguistic theory 1930-1955 // https://books.google.ru/books?id=T8LDtgAACAAJ

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Maruyama Y. (2019). Quantum Physics and Cognitive Science from a Wittgensteinian Perspective: Bohr’s Classicism, Chomsky’s Universalism, and Bell’s Contextualism / Wuppuluri S., da Costa N. (2019). WITTGENSTEINIAN (adj.). The Frontiers Collection. Springer, Cham // https://doi.org/10.1007/978-3-030-27569-3_20

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Kilgarriff A., Baisa V., Bušta J., Jakubíček M., Kovář V., Michelfeit J., Rychlý P., Suchomel V. (2014). The Sketch Engine: ten years on / Lexicography, Vol. 1, Iss. 1, pp. 7–36 // https://doi.org/10.1007/s40607-014-0009-9

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