Implications of using AI in Translation Studies: Trends, Challenges, and Future Direction
Abstract
This review paper provides an overview of the use of artificial intelligence (AI) in translation studies. The paper discusses the various AI techniques that have been used in translation, including statistical machine translation, rule-based machine translation, neural machine translation, and hybrid machine translation. The paper also explores the advantages and limitations of each model, as well as their applications in translation studies. Additionally, the paper reviews the various techniques for evaluating the effectiveness of AI models in translation and their advantages and limitations. The challenges and limitations of AI in translation, such as the handling of idioms, metaphors, and cultural nuances, are also discussed, along with research directions for improving AI-based translation. The review also discusses the representation of AI in literature and the arts, delves into the academic opportunities, and investigates its impact on human lives. Furthermore, AI's ethical and social implications in translation, such as job displacement, data privacy, and bias and fairness, are examined. Finally, the paper summarizes the main findings, implications, and recommendations for future research directions in AI-based translation studies. Overall, this review paper provides some insights into the current state of AI in translation and its potential for improving the field.
References
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Alkhatib, M., & Shaalan, K. (2018). Paraphrasing Arabic metaphor with neural machine translation. Procedia Computer Science, 142, 308-314.
Al-Thani, N. J., & Atwell, E. (2017). Using machine translation to aid Arabic-English translation. Journal of Information Science, 43(1), 111-123.
Asimov, I. (1941). Three laws of robotics. Asimov, I. Runaround, 2.
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Canfora, C., & Ottmann, A. (2020). Risks in neural machine translation. Translation Spaces, 9(1), 58-77.
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation.
Costa-Jussa, M. R., & Fonollosa, J. A. (2015). Latest trends in hybrid machine translation and its applications. Computer Speech & Language, 32(1), 3-10.
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Drugan, J., & Tipton, R. (2017). Translation, ethics and social responsibility. The translator, 23(2), 119-125.
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Firat, O., Cho, K., & Bengio, Y. (2016). Multi-way, multilingual neural machine translation with a shared attention mechanism. arXiv preprint arXiv:1601.01073.
Gambier, Y. (2016). Translations| Rapid and Radical Changes in Translation and Translation Studies. International Journal of Communication, 10, 20.
Gong, Y. (2022). Study on Machine Translation Teaching Model Based on Translation Parallel Corpus and Exploitation for Multimedia Asian Information Processing. ACM Transactions on Asian and Low-Resource Language Information Processing.
Gu, J., Hassan, H., Devlin, J., & Li, V. O. (2018). Universal neural machine translation for extremely low resource languages. arXiv preprint arXiv:1802.05368.
Haluza, D., & Jungwirth, D. (2023). Artificial Intelligence and Ten Societal Megatrends: An Exploratory Study Using GPT-3. Systems, 11(3), 120.
Hancock, J. T., Naaman, M., & Levy, K. (2020). AI-mediated communication: Definition, research agenda, and ethical considerations. Journal of Computer-Mediated Communication, 25(1), 89-100.
Hartmann, K. V., Primc, N., & Rubeis, G. (2023). Lost in translation? Conceptions of privacy and independence in the technical development of AI-based AAL. Medicine, Health Care and Philosophy, 26(1), 99-110.
Hasan, S. A., Zou, B., & Wong, D. F. (2020). A comparative study of deep learning models for machine translation. Cognitive Computation, 12(5), 857-873.
Hemmati, A., & Rahmani, A. M. (2022). The Internet of Autonomous Things applications: A taxonomy, technologies, and future directions. Internet of Things, 20, 100635.
Hutchins, W. (2000). The first decades of machine translation. Early years in machine translation, 1, 1-15.
Johnson, M., Schuster, M., Le, Q. V., Krikun, M., Wu, Y., Chen, Z., ... & Dean, J. (2017). Google’s multilingual neural machine translation system: Enabling zero-shot translation. Transactions of the Association for Computational Linguistics, 5, 339-351.
Kenny, D. (2018). Machine translation. In The Routledge handbook of translation and philosophy (pp. 428-445). Routledge.
Klein, D. (2008). Unsupervised Learning for Natural Language Processing. In COLT (pp. 5-6).
Koehn, P. (2017). Neural machine translation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (pp. 174–180).
Koehn, P. (2017). Six challenges for neural machine translation. In Proceedings of the First Workshop on Neural Machine Translation (pp. 28–39).
Lakew, S. M., Federico, M., Negri, M., & Turchi, M. (2018). Multilingual neural machine translation for low-resource languages. IJCoL. Italian Journal of Computational Linguistics, 4(4-1), 11-25.
Lample, G., Ott, M., Conneau, A., Denoyer, L., & Ranzato, M. A. (2018). Phrase-based & neural unsupervised machine translation. arXiv preprint arXiv:1804.07755.
Läubli, S., Sennrich, R., & Volk, M. (2018). Has machine translation achieved human parity? a case for document-level evaluation. arXiv preprint arXiv:1808.07048. Exploring an interdisciplinary interface. In Quality assurance and assessment practices in translation and interpreting (pp. 57-87). IGI Global.
Liu, I. C. (2023). Complexity, Environment and Future Aesthetics: From New Media to Non-Media. https://doi.org/10.31235/osf.io/njevw
Luong, M. T., & Manning, C. D. (2015). Stanford neural machine translation systems for spoken language domains. In Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign (pp. 76-79).
Mandarić, K. (2022). The Impact of Artificial Intelligence on the Translation Profession. A Case study of Microsoft Translator (Doctoral dissertation, Josip Juraj Strossmayer University of Osijek. Faculty of Humanities and Social Sciences. Department of English Language and Literature).
Massey, G., & Wieder, R. (2019). Quality assurance in translation and corporate communications: Exploring an interdisciplinary interface. In Quality assurance and assessment practices in translation and interpreting (pp. 57-87). IGI Global.
McEwan, I. (2019). Machines like me: A novel. Anchor.
Miller, A. I. (2019). The artist in the machine: The world of AI-powered creativity. Mit Press.
Moorkens, J. (2022). Ethics and machine translation. Machine translation for everyone: Empowering users in the age of artificial intelligence, 18, 121.
Muñoz-Basols, J., Neville, C., Lafford, B. A., & Godev, C. (2023). Potentialities of Applied Translation for Language Learning in the Era of Artificial Intelligence. Hispania, 106(2), 171-194.
Narayan, S., & Gardent, C. (2014, June). Hybrid simplification using deep semantics and machine translation. In The 52nd annual meeting of the association for computational linguistics (pp. 435-445).
Niederman, F., & Baker, E. W. (2023). Ethics and AI Issues: Old Container with New Wine? Information Systems Frontiers, 25(1), 9-28.
O'Hagan, M. (2016). Translations| Massively Open Translation: Unpacking the Relationship Between Technology and Translation in the 21st Century. International Journal of Communication, 10, 18.
Pessach, D., & Shmueli, E. (2022). A review on fairness in machine learning. ACM Computing Surveys (CSUR), 55(3), 1-44.
Prates, M. O., Avelar, P. H., & Lamb, L. C. (2020). Assessing gender bias in machine translation: a case study with Google translate. Neural Computing and Applications, 32, 6363-6381.
Qi, J., & Peng, Y. (2018, July). Cross-modal Bidirectional Translation via Reinforcement Learning. In IJCAI (pp. 2630-2636).
Renza, V. (2023). AI Generated Art. Morals & Machines, 2(2), 32-39.
Sayle, R. (2009). Preserving Nuance in Chemical Nomenclature Translation. The ATA Chronicle.
Sennrich, R., Haddow, B., & Birch, A. (2017). Neural machine translation of rare words with subword units. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1715–1725).
Sreelekha, S., Bhattacharyya, P., & Malathi, D. (2018). Statistical vs. rule-based machine translation: A comparative study on Indian languages. In International Conference on Intelligent Computing and Applications: ICICA 2016 (pp. 663-675). Springer Singapore.
Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial intelligence, machine learning and deep learning in advanced robotics, A review. Cognitive Robotics.
Sosoni, V., Kermanidis, K. L., Stasimioti, M., Naskos, T., Takoulidou, E., Van Zaanen, M., ... & Egg, M. (2018, May). Translation crowdsourcing: creating a multilingual corpus of online educational content. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018).
Sreelekha, S. (2017). Statistical vs rule based machine translation; a case study on Indian language perspective. In International Conference on Intelligent Computing and Applications.
Tai, M. C. T. (2020). The impact of artificial intelligence on human society and bioethics. Tzu-Chi Medical Journal, 32(4), 339.
Vaswani, A., Bengio, S., Brevdo, E., Chollet, F., Gomez, A. N., Gouws, S., ... & Uszkoreit, J. (2018). Tensor2tensor for neural machine translation. arXiv preprint arXiv:1803.07416.
Vela-Valido, J. (2021). Translation quality management in the AI age. New technologies to perform translation quality management operations. Revista Tradumàtica.
Weisz, J. D., Muller, M., Houde, S., Richards, J., Ross, S. I., Martinez, F., ... & Talamadupula, K. (2021, April). Perfection not required? Human-AI partnerships in code translation. In 26th International Conference on Intelligent User Interfaces (pp. 402-412).
Yao, S., & Wan, X. (2020, July). Multimodal transformer for multimodal machine translation. In Proceedings of the 58th annual meeting of the association for computational linguistics (pp. 4346-4350).
Zakraoui, J., Saleh, M., Al-Maadeed, S., & AlJa’am, J. M. (2020, April). Evaluation of Arabic to English machine translation systems. In 2020 11th International Conference on Information and Communication Systems (ICICS) (pp. 185-190). IEEE.
Zhang, C., & Lu, Y. (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23, 100224.
Zhao, X., & Jiang, Y. (2022). Synchronously improving multi-user English translation ability by using AI. International Journal on Artificial Intelligence Tools, 31(04), 2240007.
Zeng, J., Zhang, W., & Huang, G. (2021). The application and prospects of artificial intelligence technology in translation industry. Journal of Computer Applications, 41(2), 405–410.
Agić, Ž., Johannsen, A., Plank, B., Alonso, H. M., Schluter, N., & Søgaard, A. (2016). Multilingual projection for parsing truly low-resource languages. Transactions of the Association for Computational Linguistics, 4, 301-312.
Alam, A. (2022). Employing adaptive learning and intelligent tutoring robots for virtual classrooms and smart campuses: reforming education in the age of artificial intelligence. In Advanced Computing and Intelligent Technologies: Proceedings of ICACIT 2022 (pp. 395-406). Singapore: Springer Nature Singapore.
Alami, H., Lehoux, P., Auclair, Y., de Guise, M., Gagnon, M. P., Shaw, J., ... & Fortin, J. P. (2020). Artificial intelligence and health technology assessment: anticipating a new level of complexity. Journal of Medical Internet Research, 22(7), e17707.
Albi, A. B., & Martínez-Carrasco, R. (2019). Future-proofing legal translation: A paradigm shift for an exponential era. Legal Translation: Current Issues and Challenges in Research, Methods and Applications, 149, 187.
Al-Antari, M. A. (2023). Artificial Intelligence for Medical Diagnostics—Existing and Future AI Technology! Diagnostics, 13(4), 688.
Alkhatib, M., & Shaalan, K. (2018). Paraphrasing Arabic metaphor with neural machine translation. Procedia Computer Science, 142, 308-314.
Al-Thani, N. J., & Atwell, E. (2017). Using machine translation to aid Arabic-English translation. Journal of Information Science, 43(1), 111-123.
Asimov, I. (1941). Three laws of robotics. Asimov, I. Runaround, 2.
Barrault, L., Chung, Y. A., Meglioli, M. C., Dale, D., Dong, N., Duquenne, P. A., ... & Wang, S. (2023). SeamlessM4T-Massively Multilingual & Multimodal Machine Translation. arXiv preprint arXiv:2308.11596.
Bentivogli, L., Bisazza, A., Cettolo, M., & Federico, M. (2016). Neural versus phrase-based machine translation quality: a case study. arXiv preprint arXiv:1608.04631.
Bernardini, S., Pierrette, B., Dragos, C., van Genabith, J., Silvia, H. S., O'Brien, S., ... & Elke, T. (2020). Language service provision in the 21st century: Challenges, opportunities and educational perspectives for translation studies. Bologna Process Beyond 2020, 297-303.
Bo, L. (2023). Ethical issues for literary translation in the Era of artificial intelligence. Babel.
Bojar, O., Chatterjee, R., Federmann, C., Graham, Y., Haddow, B., Huang, S., ... & Turchi, M. (2017). Findings of the 2017 conference on machine translation (wmt17). Association for Computational Linguistics.
Borkert, M., Fisher, K. E., & Yafi, E. (2018). The best, the worst, and the hardest to find: How people, mobiles, and social media connect migrants in (to) Europe. Social Media+ Society, 4(1), 2056305118764428.
Bowker, L. (2020). Translation technology and ethics (pp. 262-278). Abingdon: Routledge.
Canfora, C., & Ottmann, A. (2020). Risks in neural machine translation. Translation Spaces, 9(1), 58-77.
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation.
Costa-Jussa, M. R., & Fonollosa, J. A. (2015). Latest trends in hybrid machine translation and its applications. Computer Speech & Language, 32(1), 3-10.
Dajun, Z., & Yun, W. (2015). Corpus-based machine translation: Its current development and perspectives. In International Forum of Teaching and Studies (Vol. 11, No. 1/2, p. 90). American Scholars Press, Inc..
Deng, J., & Chen, X. (2021). Research on artificial intelligence interaction in computer-aided arts and crafts. Mobile Information Systems, 2021, 1-14.
Dhariya, O., Malviya, S., & Tiwary, U. S. (2017, January). A hybrid approach for Hindi-English machine translation. In 2017 international conference on information networking (ICOIN) (pp. 389-394). IEEE.
Dick, P. K. (2014). Do Androids Dream of Electric Sheep. Simon and Schuster.
Drugan, J., & Tipton, R. (2017). Translation, ethics and social responsibility. The translator, 23(2), 119-125.
Duarte, A. C. (2019, October). Cross-modal neural sign language translation. In Proceedings of the 27th ACM International Conference on Multimedia (pp. 1650-1654).
Firat, O., Cho, K., & Bengio, Y. (2016). Multi-way, multilingual neural machine translation with a shared attention mechanism. arXiv preprint arXiv:1601.01073.
Gambier, Y. (2016). Translations| Rapid and Radical Changes in Translation and Translation Studies. International Journal of Communication, 10, 20.
Gong, Y. (2022). Study on Machine Translation Teaching Model Based on Translation Parallel Corpus and Exploitation for Multimedia Asian Information Processing. ACM Transactions on Asian and Low-Resource Language Information Processing.
Gu, J., Hassan, H., Devlin, J., & Li, V. O. (2018). Universal neural machine translation for extremely low resource languages. arXiv preprint arXiv:1802.05368.
Haluza, D., & Jungwirth, D. (2023). Artificial Intelligence and Ten Societal Megatrends: An Exploratory Study Using GPT-3. Systems, 11(3), 120.
Hancock, J. T., Naaman, M., & Levy, K. (2020). AI-mediated communication: Definition, research agenda, and ethical considerations. Journal of Computer-Mediated Communication, 25(1), 89-100.
Hartmann, K. V., Primc, N., & Rubeis, G. (2023). Lost in translation? Conceptions of privacy and independence in the technical development of AI-based AAL. Medicine, Health Care and Philosophy, 26(1), 99-110.
Hasan, S. A., Zou, B., & Wong, D. F. (2020). A comparative study of deep learning models for machine translation. Cognitive Computation, 12(5), 857-873.
Hemmati, A., & Rahmani, A. M. (2022). The Internet of Autonomous Things applications: A taxonomy, technologies, and future directions. Internet of Things, 20, 100635.
Hutchins, W. (2000). The first decades of machine translation. Early years in machine translation, 1, 1-15.
Johnson, M., Schuster, M., Le, Q. V., Krikun, M., Wu, Y., Chen, Z., ... & Dean, J. (2017). Google’s multilingual neural machine translation system: Enabling zero-shot translation. Transactions of the Association for Computational Linguistics, 5, 339-351.
Kenny, D. (2018). Machine translation. In The Routledge handbook of translation and philosophy (pp. 428-445). Routledge.
Klein, D. (2008). Unsupervised Learning for Natural Language Processing. In COLT (pp. 5-6).
Koehn, P. (2017). Neural machine translation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (pp. 174–180).
Koehn, P. (2017). Six challenges for neural machine translation. In Proceedings of the First Workshop on Neural Machine Translation (pp. 28–39).
Lakew, S. M., Federico, M., Negri, M., & Turchi, M. (2018). Multilingual neural machine translation for low-resource languages. IJCoL. Italian Journal of Computational Linguistics, 4(4-1), 11-25.
Lample, G., Ott, M., Conneau, A., Denoyer, L., & Ranzato, M. A. (2018). Phrase-based & neural unsupervised machine translation. arXiv preprint arXiv:1804.07755.
Läubli, S., Sennrich, R., & Volk, M. (2018). Has machine translation achieved human parity? a case for document-level evaluation. arXiv preprint arXiv:1808.07048. Exploring an interdisciplinary interface. In Quality assurance and assessment practices in translation and interpreting (pp. 57-87). IGI Global.
Liu, I. C. (2023). Complexity, Environment and Future Aesthetics: From New Media to Non-Media. https://doi.org/10.31235/osf.io/njevw
Luong, M. T., & Manning, C. D. (2015). Stanford neural machine translation systems for spoken language domains. In Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign (pp. 76-79).
Mandarić, K. (2022). The Impact of Artificial Intelligence on the Translation Profession. A Case study of Microsoft Translator (Doctoral dissertation, Josip Juraj Strossmayer University of Osijek. Faculty of Humanities and Social Sciences. Department of English Language and Literature).
Massey, G., & Wieder, R. (2019). Quality assurance in translation and corporate communications: Exploring an interdisciplinary interface. In Quality assurance and assessment practices in translation and interpreting (pp. 57-87). IGI Global.
McEwan, I. (2019). Machines like me: A novel. Anchor.
Miller, A. I. (2019). The artist in the machine: The world of AI-powered creativity. Mit Press.
Moorkens, J. (2022). Ethics and machine translation. Machine translation for everyone: Empowering users in the age of artificial intelligence, 18, 121.
Muñoz-Basols, J., Neville, C., Lafford, B. A., & Godev, C. (2023). Potentialities of Applied Translation for Language Learning in the Era of Artificial Intelligence. Hispania, 106(2), 171-194.
Narayan, S., & Gardent, C. (2014, June). Hybrid simplification using deep semantics and machine translation. In The 52nd annual meeting of the association for computational linguistics (pp. 435-445).
Niederman, F., & Baker, E. W. (2023). Ethics and AI Issues: Old Container with New Wine? Information Systems Frontiers, 25(1), 9-28.
O'Hagan, M. (2016). Translations| Massively Open Translation: Unpacking the Relationship Between Technology and Translation in the 21st Century. International Journal of Communication, 10, 18.
Pessach, D., & Shmueli, E. (2022). A review on fairness in machine learning. ACM Computing Surveys (CSUR), 55(3), 1-44.
Prates, M. O., Avelar, P. H., & Lamb, L. C. (2020). Assessing gender bias in machine translation: a case study with Google translate. Neural Computing and Applications, 32, 6363-6381.
Qi, J., & Peng, Y. (2018, July). Cross-modal Bidirectional Translation via Reinforcement Learning. In IJCAI (pp. 2630-2636).
Renza, V. (2023). AI Generated Art. Morals & Machines, 2(2), 32-39.
Sayle, R. (2009). Preserving Nuance in Chemical Nomenclature Translation. The ATA Chronicle.
Sennrich, R., Haddow, B., & Birch, A. (2017). Neural machine translation of rare words with subword units. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1715–1725).
Sreelekha, S., Bhattacharyya, P., & Malathi, D. (2018). Statistical vs. rule-based machine translation: A comparative study on Indian languages. In International Conference on Intelligent Computing and Applications: ICICA 2016 (pp. 663-675). Springer Singapore.
Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial intelligence, machine learning and deep learning in advanced robotics, A review. Cognitive Robotics.
Sosoni, V., Kermanidis, K. L., Stasimioti, M., Naskos, T., Takoulidou, E., Van Zaanen, M., ... & Egg, M. (2018, May). Translation crowdsourcing: creating a multilingual corpus of online educational content. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018).
Sreelekha, S. (2017). Statistical vs rule based machine translation; a case study on Indian language perspective. In International Conference on Intelligent Computing and Applications.
Tai, M. C. T. (2020). The impact of artificial intelligence on human society and bioethics. Tzu-Chi Medical Journal, 32(4), 339.
Vaswani, A., Bengio, S., Brevdo, E., Chollet, F., Gomez, A. N., Gouws, S., ... & Uszkoreit, J. (2018). Tensor2tensor for neural machine translation. arXiv preprint arXiv:1803.07416.
Vela-Valido, J. (2021). Translation quality management in the AI age. New technologies to perform translation quality management operations. Revista Tradumàtica.
Weisz, J. D., Muller, M., Houde, S., Richards, J., Ross, S. I., Martinez, F., ... & Talamadupula, K. (2021, April). Perfection not required? Human-AI partnerships in code translation. In 26th International Conference on Intelligent User Interfaces (pp. 402-412).
Yao, S., & Wan, X. (2020, July). Multimodal transformer for multimodal machine translation. In Proceedings of the 58th annual meeting of the association for computational linguistics (pp. 4346-4350).
Zakraoui, J., Saleh, M., Al-Maadeed, S., & AlJa’am, J. M. (2020, April). Evaluation of Arabic to English machine translation systems. In 2020 11th International Conference on Information and Communication Systems (ICICS) (pp. 185-190). IEEE.
Zhang, C., & Lu, Y. (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23, 100224.
Zhao, X., & Jiang, Y. (2022). Synchronously improving multi-user English translation ability by using AI. International Journal on Artificial Intelligence Tools, 31(04), 2240007.
Zeng, J., Zhang, W., & Huang, G. (2021). The application and prospects of artificial intelligence technology in translation industry. Journal of Computer Applications, 41(2), 405–410.
Published
2024-04-30
How to Cite
AMINI, Mansour; RAVINDRAN, Latha; LEE, Kam-Fong.
Implications of using AI in Translation Studies: Trends, Challenges, and Future Direction.
Asian Journal of Research in Education and Social Sciences, [S.l.], v. 6, n. 1, p. 740-754, apr. 2024.
Available at: <https://myjms.mohe.gov.my/index.php/ajress/article/view/24617>. Date accessed: 19 sep. 2024.
Section
English Section
