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- First Online: 09 September 2024
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- Assem Shormakova ORCID: orcid.org/0000-0002-1637-4643 10 &
- Ualsher Tukeyev ORCID: orcid.org/0000-0001-9878-981X 10
Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2166)) Included in the following conference series: - International Conference on Computational Collective Intelligence
The work presents a model, algorithm, and experimental studies for selecting synonyms of incorrectly translated words from English into Kazakh within the framework of machine translation post-editing technology. As a model for choosing synonyms, a maximum entropy model has been developed, the distinctive feature of which is the consideration of contextual words located at any distance from the translated word in a sentence (non-consecutive collocations), which takes into account the peculiarities of the Kazakh language. A feature of the proposed solution algorithm for this model is the use of the semantic cube model proposed by the authors. The developed maximum entropy model was learned on the 250 0000 sentences parallel Kazakh-English corpus, and a test set containing 25,000 sentences was conducted. Experiments on post-editing of machine translation of the Kazakh language compared with machine translation of Google Translate showed an improvement in the BLEU metric by 6 positions. This is a preview of subscription content, log in via an institution to check access. Access this chapterSubscribe and save. - Get 10 units per month
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Tax calculation will be finalised at checkout Purchases are for personal use only Institutional subscriptions Koponen, M.: Machine translation post-editing and effort: empirical studies on the post-editing process. Doctoral thesis, University of Helsinki, Helsinki, Finland (2016). http://hdl.handle.net/10138/160256 . Accessed 18 Feb 2022 Shormakova, A.N., Tukeyev, U.A.: Machine translation technology with teaching English into Kazakh. In: Materials of the International Conference of Students and Young Scientists “The World of Science”, 23–26 April 2012, p. 154. Almaty: Kazakh University (2012) Google Scholar Papineni, K., Roukos, S., Ward, T., Henderson, J., Reeder, F.: Corpus-based comprehensive and diagnostic MT evaluation: initial Arabic, Chinese, French, and Spanish results. In: Proceedings of Human Language Technology 2002, San Diego, CA (2002) Barrault, L., et al.: Findings of the 2019 conference on machine translation (WMT19). 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CoRR abs/1803.07274 (2018) Gois, A., Cho, K., Martins, A.: Learning non-monotonic automatic post-editing of translations from human orderings. In: Proceedings of the 22nd Annual Conference of the European Association for Machine Translation (EAMT 2020), pp. 205–214, Lisboa, Portugal, November 2020 do Carmo, Félix., et al.: A review of the state-of-the-art in automatic post-editing. Mach. Transl. 35 (2), 101–143 (2020). https://doi.org/10.1007/s10590-020-09252-y Chatterjee, R., Freitag, M., Negri, M., Turchi, M.: Findings of the WMT 2020 shared task on automatic post-editing. In: Proceedings of the WMT 2020 Automatic Post-Editing Shared Task (2020). https://www.statmt.org/wmt20/pdf/2020.wmt-1.75.pdf . Accessed 18 Feb 2022 Yang, H., et al.: HW-TSC’s participation at WMT 2020 automatic post editing shared task. In: Proceedings of the WMT 2020 Automatic Post Editing Shared Task (2020). https://aclanthology.org/2020.wmt-1.85.pdf . 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Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67077-5_47 Chapter Google Scholar Berger, A.L., Pietra, S.A.D., Pietra, V.J.D.: A maximum entropy approach to natural language processing. Comput. Linguist. 22 , 39–71 (1996) Jurafsky, D., Martin, J.H.: Automatic Speech Recognition. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, pp. 213–220. Prentice Hall, Upper Saddle River A Study of Translation Edit Rate with Targeted Human Annotation (TER). https://www.cs.umd.edu/~snover/pub/amta06/ter_amta.pdf . Accessed 18 May 2022 Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of Association for Computational Linguistics (ACL 2002), pp. 311–318, Association for Computational Linguistics, Philadelphia, Pennsylvania, USA (2002) Post, M.: A call for clarity in reporting BLEU scores. In: Proceedings of the Third Conference on Machine Translation: Research Papers, pp. 186–191. Association for Computational Linguistics, Brussels, Belgium (2018). Download references Author informationAuthors and affiliations. Al-Farabi Kazakh National University, Almaty, Kazakhstan Assem Shormakova & Ualsher Tukeyev You can also search for this author in PubMed Google Scholar Corresponding authorCorrespondence to Assem Shormakova . Editor informationEditors and affiliations. and Technology, Wrocław University of Science, Wrocław, Poland Ngoc-Thanh Nguyen University of Leipzig, Leipzig, Germany Bogdan Franczyk University of Leipzig, Leipzig, Sachsen, Germany André Ludwig Universidad Complutense de Madrid, Madrid, Spain Manuel Nunez Vrije Universiteit Amsterdam, Amsterdam, The Netherlands University of Münster, Münster, Germany Gottfried Vossen Adrianna Kozierkiewicz Rights and permissionsReprints and permissions Copyright information© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG About this paperCite this paper. Shormakova, A., Tukeyev, U. (2024). Maximum Entropy Model of Synonym Selection in Post-editing Machine Translation into Kazakh Language. In: Nguyen, NT., et al. Advances in Computational Collective Intelligence. ICCCI 2024. Communications in Computer and Information Science, vol 2166. Springer, Cham. https://doi.org/10.1007/978-3-031-70259-4_9 Download citationDOI : https://doi.org/10.1007/978-3-031-70259-4_9 Published : 09 September 2024 Publisher Name : Springer, Cham Print ISBN : 978-3-031-70258-7 Online ISBN : 978-3-031-70259-4 eBook Packages : Computer Science Computer Science (R0) Share this paperAnyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative Policies and ethics - Find a journal
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The work presents a model, algorithm, and experimental studies for selecting synonyms of incorrectly translated words from English into Kazakh within the framework of machine translation post-editing technology. As a model for choosing synonyms, a maximum entropy model has been developed, the distinctive feature of which is the consideration of ...
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