ARTIFICIAL INTELLIGENCE TO AUTOMATE: TRANSLATION OF TECHNICAL TERMS IN PROJECT MANAGEMENT
Keywords:
artificial intelligence, machine translation, technical terms, project management, translation automation
Abstract
This article highlights the influence of artificial intelligence (AI) on automating the translation of technical terminology in project management. In particular, the article focuses on the use of machine translation (MT), which, thanks to their ability to process large amounts of data, cloud computing, and advanced algorithms, increase the accuracy and speed of translation of technical documentation. It is noted that AI helps to reduce translation costs and improves the consistency of terminology, which is critical for the successful completion of projects with tight deadlines. Nevertheless, challenges in this field are also identified, including the high error rate in translating highly specialized terms and the ongoing need to enhance systems to meet the varied demands of users. Different translation approaches are discussed, including neural machine translation (NMT), statistical machine translation (SMT) and rule-based machine translation (RBMT), which ensure high accuracy and smoothness of translation. Integrating AI into project management systems can also optimise communication in multilingual teams. The article highlights the growing role of AI in the translation of technical terms, which has great potential to improve efficiency in project management.
References
1. Aldawsar, H A H (2024). Evaluating Translation Tools: Google Translate, Bing Translator, and Bing AI on Arabic Colloquialisms. Arab World English Journal. https://doi.org/10.24093/awej/chatgpt.16
2. Aramaki, E, Kurohashi, S (2004). Example-based machine translation using structural translation examples. https://www.semanticscholar.org/paper/b1120b76fcd549fe7bfc9f6f577bd98e0fe9d7aa
3. Artetxe, M et al. (2018). Unsupervised Statistical Machine Translation. https://doi.org/10.18653/v1/D18-1399
4. Bhattacharyya, P et al. (2016). Statistical Machine Translation between Related Languages. https://doi.org/10.18653/V1/N16-4006
5. Charoenpornsawat, P et al. (2002). Improving Translation Quality of Rule-based Machine Translation. https://doi.org/10.3115/1118794.1118799
6. Das, S B et al. (2023). Statistical Machine Translation for Indic Languages. ArXiv, abs/2301.00539. https://doi.org/10.1017/nlp.2024.26
7. Hassan, H, Darwish, K (2014). Statistical Machine Translation. https://doi.org/10.1007/978-3-642-45358-8_6
8. Implications of using AI in Translation Studies: Trends, Challenges, and Future Direction. Asian Journal of Research in Education and Social Sciences. 2024. https://doi.org/10.55057/ajress.2024.6.1.67
9. Ju, L, Salvosa, A A (2024). Research and Optimization of English Automatic Translation System Based on Machine Learning Algorithm. 2024 9th International Symposium on Computer and Information Processing Technology (ISCIPT), 1-5. https://doi.org/10.1109/ISCIPT61983.2024.10673006
10. Karamthulla, M J et al. (2024). Navigating the Future: AI-Driven Project Management in the Digital Era. International Journal For Multidisciplinary Research. https://doi.org/10.36948/ijfmr.2024.v06i02.15295
11. Kolhar, M S, Alameen, A (2021). Artificial Intelligence Based Language Translation Platform. Intelligent Automation & Soft Computing. https://doi.org/10.32604/IASC.2021.014995
12. Kononova, I (2024). Artificial intelligence in the field of electronic communications. XVIII International Scientific Conference "Modern Challenges in Telecommunications" MCT-2024. Conference proceedings. Kyiv. Igor Sikorsky Kyiv Polytechnic Institute
13. Lee, T (2023). Artificial intelligence and posthumanist translation: ChatGPT versus the translator. Applied Linguistics Review, 0. https://doi.org/10.1515/applirev-2023-0122
14. Li, R et al. (2023). Human-machine Translation Model Evaluation Based on Artificial Intelligence Translation. EMITTER International Journal of Engineering Technology. https://doi.org/10.24003/emitter.v11i2.812
15. Luong, M (2016). Neural Machine Translation. International Journal for Research in Applied Science and Engineering Technology. https://doi.org/10.5565/rev/tradumatica.203
16. Macketanz, V et al. (2017). Machine Translation: Phrase-Based, Rule-Based and Neural Approaches with Linguistic Evaluation. Cybernetics and Information Technologies, 17, 28 – 43. https://doi.org/10.1515/cait-2017-0014
17. Mahata, S et al. (2019). MTIL2017: Machine Translation Using Recurrent Neural Network on Statistical Machine Translation. Journal of Intelligent Systems, 28, 447 – 453. https://doi.org/10.1515/jisys-2018-0016
18. Martins, M M R (2023). Project Management Evolution: From Traditional IT Implementations to AI-Driven Projects. International Journal of Scientific Research and Management (IJSRM) https://doi.org/10.18535/ijsrm/v11i07.em03
19. Mishra, K., Kanojia, M., & Shaikh, A. (2023). LSTM-Based Model for Sanskrit to English Translation. У Intelligent Systems Design and Applications (с. 219–226). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-35501-1_22
20. Mohamed, Y. A., Kannan, A., Bashir, M., Mohamed, A. H., Adiel, M. A. E., & Elsadig, M. A. (2024). The Impact of Artificial Intelligence on Language Translation: A review. IEEE Access, 1. https://doi.org/10.1109/access.2024.3366802
21. Nair, M et al. (2023). Use of Neural Machine Translation in Multimodal Translation. 2023 6th International Conference on Contemporary Computing and Informatics (IC3I), 6, 130-135. https://doi.org/10.1109/IC3I59117.2023.10397780
22. Niehues, J et al. (2016). Pre-Translation for Neural Machine Translation. ArXiv, abs/1610.05243. https://www.semanticscholar.org/paper/d2f9cab490a506e2574e9d8a15bc20a181ff999b
23. Raj, D et al. (2024). Impact of an Artificial Intelligence in Language Learning – A survey. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. https://doi.org/10.32628/cseit2410218
24. Savio, R D, Ali, J M (2023). Artificial Intelligence in Project Management & Its Future. Saudi Journal of Engineering and Technology. https://doi.org/10.36348/sjet.2023.v08i10.002
25. Schwenk, H (2012). Continuous Space Translation Models for Phrase-Based Statistical Machine Translation. https://www.semanticscholar.org/paper/5f08df805f14baa826dbddcb002277b15d3f1556
26. Sghaier, M A, Zrigui, M (2020). Rule-Based Machine Translation from Tunisian Dialect to Modern Standard Arabic. https://doi.org/10.1016/j.procs.2020.08.033
27. Subtitling Legal Expressions in English Series into Arabic by Netflix, Machine, and Artificial Intelligence. Pakistan Journal of Criminology. 2024. https://doi.org/10.62271/pjc.16.4.513.528
28. Sumita, E (2001). Example-based machine translation using DP-matching between work sequences. https://doi.org/10.3115/1118037.1118038
29. Tang, H (2024). Integration and Innovation of Artificial Intelligence and Traditional English Translation Methods. Applied Mathematics and Nonlinear Sciences, 9. https://doi.org/10.2478/amns-2024-1575
30. Torregrosa, D et al. (2020). Aspects of Terminological and Named Entity Knowledge withinRule-Based Machine Translation Models for Under-Resourced Neural Machine Translation Scenarios. ArXiv, abs/2009.13398. https://www.semanticscholar.org/paper/cf3d82da99aca5b-66cb17490f88e46bec0f9fdbb
31. Um, E N et al. (2022). Developing a Rule-Based Machine-Translation System, Ewondo-French-Ewondo. Int. J. Humanit. Arts Comput., 16, 166-181. https://doi.org/10.3366/ijhac.2022.0289
32. Vo, H N K et al. (2024). Revitalizing Bahnaric Language through Neural Machine Translation: Challenges, Strategies, and Promising Outcomes. https://doi.org/10.1609/aaai.v38i21.30385
33. Wang, X et al. (2016). Neural Machine Translation Advised by Statistical Machine Translation. https://doi.org/10.1609/aaai.v31i1.10975
34. Xu, J et al. (2020). Boosting Neural Machine Translation with Similar Translations. https://doi.org/10.18653/v1/2020.acl-main.144
35. Yang, Q (2021). Profiling Artificial Intelligence as a Material for User Experience Design. https://doi.org/10.1184/R1/14376731.V1
36. Yang, X et al. (2024). English Translation System Assisted by Machine Learning and Cloud Computing Data Aggregation Algorithm. 2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), 1-5. https://doi.org/10.1109/ICDCECE60827.2024.10549679
37. Zhang, B et al. (2016). Variational Neural Machine Translation. https://doi.org/10.18653/v1/D16-1050
2. Aramaki, E, Kurohashi, S (2004). Example-based machine translation using structural translation examples. https://www.semanticscholar.org/paper/b1120b76fcd549fe7bfc9f6f577bd98e0fe9d7aa
3. Artetxe, M et al. (2018). Unsupervised Statistical Machine Translation. https://doi.org/10.18653/v1/D18-1399
4. Bhattacharyya, P et al. (2016). Statistical Machine Translation between Related Languages. https://doi.org/10.18653/V1/N16-4006
5. Charoenpornsawat, P et al. (2002). Improving Translation Quality of Rule-based Machine Translation. https://doi.org/10.3115/1118794.1118799
6. Das, S B et al. (2023). Statistical Machine Translation for Indic Languages. ArXiv, abs/2301.00539. https://doi.org/10.1017/nlp.2024.26
7. Hassan, H, Darwish, K (2014). Statistical Machine Translation. https://doi.org/10.1007/978-3-642-45358-8_6
8. Implications of using AI in Translation Studies: Trends, Challenges, and Future Direction. Asian Journal of Research in Education and Social Sciences. 2024. https://doi.org/10.55057/ajress.2024.6.1.67
9. Ju, L, Salvosa, A A (2024). Research and Optimization of English Automatic Translation System Based on Machine Learning Algorithm. 2024 9th International Symposium on Computer and Information Processing Technology (ISCIPT), 1-5. https://doi.org/10.1109/ISCIPT61983.2024.10673006
10. Karamthulla, M J et al. (2024). Navigating the Future: AI-Driven Project Management in the Digital Era. International Journal For Multidisciplinary Research. https://doi.org/10.36948/ijfmr.2024.v06i02.15295
11. Kolhar, M S, Alameen, A (2021). Artificial Intelligence Based Language Translation Platform. Intelligent Automation & Soft Computing. https://doi.org/10.32604/IASC.2021.014995
12. Kononova, I (2024). Artificial intelligence in the field of electronic communications. XVIII International Scientific Conference "Modern Challenges in Telecommunications" MCT-2024. Conference proceedings. Kyiv. Igor Sikorsky Kyiv Polytechnic Institute
13. Lee, T (2023). Artificial intelligence and posthumanist translation: ChatGPT versus the translator. Applied Linguistics Review, 0. https://doi.org/10.1515/applirev-2023-0122
14. Li, R et al. (2023). Human-machine Translation Model Evaluation Based on Artificial Intelligence Translation. EMITTER International Journal of Engineering Technology. https://doi.org/10.24003/emitter.v11i2.812
15. Luong, M (2016). Neural Machine Translation. International Journal for Research in Applied Science and Engineering Technology. https://doi.org/10.5565/rev/tradumatica.203
16. Macketanz, V et al. (2017). Machine Translation: Phrase-Based, Rule-Based and Neural Approaches with Linguistic Evaluation. Cybernetics and Information Technologies, 17, 28 – 43. https://doi.org/10.1515/cait-2017-0014
17. Mahata, S et al. (2019). MTIL2017: Machine Translation Using Recurrent Neural Network on Statistical Machine Translation. Journal of Intelligent Systems, 28, 447 – 453. https://doi.org/10.1515/jisys-2018-0016
18. Martins, M M R (2023). Project Management Evolution: From Traditional IT Implementations to AI-Driven Projects. International Journal of Scientific Research and Management (IJSRM) https://doi.org/10.18535/ijsrm/v11i07.em03
19. Mishra, K., Kanojia, M., & Shaikh, A. (2023). LSTM-Based Model for Sanskrit to English Translation. У Intelligent Systems Design and Applications (с. 219–226). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-35501-1_22
20. Mohamed, Y. A., Kannan, A., Bashir, M., Mohamed, A. H., Adiel, M. A. E., & Elsadig, M. A. (2024). The Impact of Artificial Intelligence on Language Translation: A review. IEEE Access, 1. https://doi.org/10.1109/access.2024.3366802
21. Nair, M et al. (2023). Use of Neural Machine Translation in Multimodal Translation. 2023 6th International Conference on Contemporary Computing and Informatics (IC3I), 6, 130-135. https://doi.org/10.1109/IC3I59117.2023.10397780
22. Niehues, J et al. (2016). Pre-Translation for Neural Machine Translation. ArXiv, abs/1610.05243. https://www.semanticscholar.org/paper/d2f9cab490a506e2574e9d8a15bc20a181ff999b
23. Raj, D et al. (2024). Impact of an Artificial Intelligence in Language Learning – A survey. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. https://doi.org/10.32628/cseit2410218
24. Savio, R D, Ali, J M (2023). Artificial Intelligence in Project Management & Its Future. Saudi Journal of Engineering and Technology. https://doi.org/10.36348/sjet.2023.v08i10.002
25. Schwenk, H (2012). Continuous Space Translation Models for Phrase-Based Statistical Machine Translation. https://www.semanticscholar.org/paper/5f08df805f14baa826dbddcb002277b15d3f1556
26. Sghaier, M A, Zrigui, M (2020). Rule-Based Machine Translation from Tunisian Dialect to Modern Standard Arabic. https://doi.org/10.1016/j.procs.2020.08.033
27. Subtitling Legal Expressions in English Series into Arabic by Netflix, Machine, and Artificial Intelligence. Pakistan Journal of Criminology. 2024. https://doi.org/10.62271/pjc.16.4.513.528
28. Sumita, E (2001). Example-based machine translation using DP-matching between work sequences. https://doi.org/10.3115/1118037.1118038
29. Tang, H (2024). Integration and Innovation of Artificial Intelligence and Traditional English Translation Methods. Applied Mathematics and Nonlinear Sciences, 9. https://doi.org/10.2478/amns-2024-1575
30. Torregrosa, D et al. (2020). Aspects of Terminological and Named Entity Knowledge withinRule-Based Machine Translation Models for Under-Resourced Neural Machine Translation Scenarios. ArXiv, abs/2009.13398. https://www.semanticscholar.org/paper/cf3d82da99aca5b-66cb17490f88e46bec0f9fdbb
31. Um, E N et al. (2022). Developing a Rule-Based Machine-Translation System, Ewondo-French-Ewondo. Int. J. Humanit. Arts Comput., 16, 166-181. https://doi.org/10.3366/ijhac.2022.0289
32. Vo, H N K et al. (2024). Revitalizing Bahnaric Language through Neural Machine Translation: Challenges, Strategies, and Promising Outcomes. https://doi.org/10.1609/aaai.v38i21.30385
33. Wang, X et al. (2016). Neural Machine Translation Advised by Statistical Machine Translation. https://doi.org/10.1609/aaai.v31i1.10975
34. Xu, J et al. (2020). Boosting Neural Machine Translation with Similar Translations. https://doi.org/10.18653/v1/2020.acl-main.144
35. Yang, Q (2021). Profiling Artificial Intelligence as a Material for User Experience Design. https://doi.org/10.1184/R1/14376731.V1
36. Yang, X et al. (2024). English Translation System Assisted by Machine Learning and Cloud Computing Data Aggregation Algorithm. 2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), 1-5. https://doi.org/10.1109/ICDCECE60827.2024.10549679
37. Zhang, B et al. (2016). Variational Neural Machine Translation. https://doi.org/10.18653/v1/D16-1050
Abstract views: 7 PDF Downloads: 7
Published
2024-12-25
How to Cite
Lysychenko, O. (2024). ARTIFICIAL INTELLIGENCE TO AUTOMATE: TRANSLATION OF TECHNICAL TERMS IN PROJECT MANAGEMENT. Scientific Journal of Polonia University, 66(5), 25-31. https://doi.org/10.23856/6603
Section
LANGUAGE, CULTURE, COMMUNICATION
