METHOD OF SYSTEM ENGINEERING OF NEURAL MACHINE TRANSLATION SYSTEMS
Keywords:method, neural machine translation system, system engineering, Erickson-Penker method
Background. There are not many machine translation companies on the market whose products are in demand. These are, for example, free and commercial products such as “GoogleTranslate”, “DeepLTranslator”, “ModernMT”, “Apertium”, “Trident”, to name a few. To implement a more efficient and productive process for developing high-quality neural machine translation systems (NMTS), appropriate scientifically based methods of NMTS engineering are needed in order to get a high-quality and competitive product as quickly as possible.
Objective. The purpose of this article is to apply the Eriksson-Penker business profile to the development and formalization of a method for system engineering of NMTS.
Methods. The idea behind the neural machine translation system engineering method is to apply the Eriksson-Penker system engineering methodology and business profile to formalize an ordered way to develop NMT systems.
Results. The method of developing NMT systems based on the use of system engineering techniques consists of three main stages. At the first stage, the structure of the NMT system is modelled in the form of an Eriksson-Penker business profile. At the second stage, a set of processes is determined that is specific to the class of Data Science systems, and the international CRISP-DM standard. At the third stage, verification and validation of the developed NMTS is carried out.
Conclusions. The article proposes a method of system engineering of NMTS based on the modified Erickson-Penker business profile representation of the system at the meta-level, as well as international process standards of Data Science and Data Mining. The effectiveness of using this method was studied on the example of developing a bidirectional English-Ukrainian NMTS EUMT (English-Ukrainian Machine Translator) and it was found that the EUMT system is at least as good as the quality of English-Ukrainian translation of the popular Google Translate translator. The full version code of the EUMT system is published on the GitHub platform and is available at: https://github.com/EugeneSel/EUMT.
H.-E. Eriksson and M. Penker, Business modeling with UML. New York: John Wiley & Sons, 2000, 459 p.
A. Kossiakoff et al., Systems Engineering Principles and Practice, V.K. Batovrin, Ed. Moscow, Russia: DMK Press, 2014, 624 p.
D.K. Hitchins, Systems Engineering: A 21st Century Systems Methodology. Wiley, 2007, 528 p.
S. Krymskyi, “Metod,” in Filosofskyi Entsyklopedychnyi Slovnyk, V.I. Shynkaruk, Ed. Kyiv, Ukraine: Abrys, 2002, 742 p.
P.Р. Maslyanko and O.S. Maystrenko, “The system engineering of organizational system informatization projects,” KPI Sci. News, no. 6, pp. 34–42, 2008.
C. O'Neil and R. Schutt, Doing data science: Straight talk from the frontline. O'Reilly Media, Inc., 2013, 406 p.
F. Provost and T. Fawcett, Data science for business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc., 2013.
D. Bahdanau et al., “Neural machine translation by jointly learning to align and translate,” in 3rd International Conference on Learning Representations, San Diego, United States, 2014.
J. Gehring et al. (2016). A convolutional encoder model for neural machine translation [Online]. Available: https://arxiv.org/pdf/1611.02344.pdf
A. Vaswani et al., “Attention is all you need,” in 31st Conference on Neural Information Processing Systems, Long Beach, CA, USA, 2017.
P. Bojanowski et al., “Enriching word vectors with subword information,” Trans. Assoc. Computation. Ling., vol. 5, pp. 135–146, 2017.
D.P. Kingma and J.L. Ba. (2014). Adam: A method for stochastic optimization [Online]. Available: https://arxiv.org/pdf/1412.6980.pdf?source=post_page
C. Szegedy et al. (2016). Rethinking the inception architecture for computer vision [Online]. Available: https://arxiv.org/pdf/1512.00567.pdf
K. Papineni et al. “Bleu: A method for automatic evaluation of machine translation,” in Proc. 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, 2002, pp. 311–318.
J. Tiedemann. (2012). Parallel data, tools and interfaces in OPUS [Online]. Available: http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
M. Johnson et al. (2017). Google’s multilingual neural machine translation system: Enabling zero-shot translation [Online]. Available: https://arxiv.org/pdf/1611.04558.pdf
S. Holger et al. (2019). WikiMatrix: Mining 135M parallel sentences in 1620 language pairs from Wikipedia [Online]. Available: https://arxiv.org/pdf/1907.05791.pdf
A. El-Kishky et al. (2021). XLEnt: Mining Cross-lingual Entities with Lexical-Semantic-Phonetic Word Alignment [Online]. Available: http://data.statmt.org/xlent/elkishky_XLEnt.pdf
A. Abdelali et al., “The AMARA Corpus: Building parallel language resources for the educational domain,” in Proc. 9th International Conference on Language Resources and Evaluation (LREC'14), Reykjavik, Iceland, 2014, p. 1856–1862.
Copyright (c) 2021 Pavlo P. Maslianko, Yevhenii P. Sielskyi
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under CC BY 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work