The Transformative Role of Artificial Intelligence and Machine Learning in Interpreting and Language Services

Abstract

The integration of artifical intelligence (AI) and machine learning (ML) into language services has ushered in a new era of rapid communication, transcending linguistic barriers and making multilingual content more accessible. These technologies are no longer confined to mere automation but have evolved into sophisticated tools that contribute significantly to the quality, speed, and diversity of language-related tasks. However, there are a number of aspects to be taken into consideration when addressing the notion of such new technologies to grasp the topic from a holistic point of view. This article delves into the manifold ways AI and ML are being utilized in interpreting, translation, and other language services, while acknowledging the ethical and practical considerations that accompany their implementation. This study aims to provide insights into the potential benefits and challenges of using AI in the interpreting industry and contribute to the understanding of how AI can enhance language services. The research question of this article is what are the ethical and practical considerations associated with the implementation of AI and ML technologies in language services, and how can these technologies be responsibly integrated into the interpreting industry? One of the remote simultaneous interpreting platforms- Interprefy’s recently launched virtual AI event interpreter Aivia will be examined and analysed in the article as a case study with possible implications for the interpreting industry and a number of suggestions for the utilization of such technologies will be shared.

Keywords

• Artifical Intelligence (AI)
• Machine Learning (ML)
• interpreting technologies
• Interprefy
• virtual interpreting assistant

References

1. Bowker, L., & Ciro, J. B. (2019). Machine Translation and Global Research: Towards Improved Machine Translation Literacy in the Scholarly Community.
2. Diaz Cintas, J., Szarkowska A. (2020). Introduction: Experimental Research in Audiovisual Translation- Cognition, Reception, Production. The Journal of Specialized Translation. 33:3-16.
3. Diriker, E. (2015). On the evaluation of the interpreting profession in Turkey from the Dragomans to the 21st century. In Ş. Tahir Gürçağlar, S. Paker & J. Milton (Eds.), Tradition, Tension and Translation in Turkey (pp.87-103). John Benjamins Publishing Company.
4. Koehn, P. (2017). Neural Machine Translation. arXiv preprint arXiv:1709.07809.
5. Liang, L. (2022). Illuminating humanist nature in teaching translation and interpreting studies. Devising an online customisable AI-driven subtitling course. Humanities and Social Sciences Communications, 9, https://www.nature.com/articles/s41599-022-01397-w.
6. Lommel, A., & García-Martínez, M. (2020). "Artificial Intelligence and Language Services: Market Developments and Opportunities." European Language Industry Association (ELIA) Focus on Artificial Intelligence.
7. O'Hagan, M., & Ashworth, D. (Eds.). (2014). The Routledge Handbook of Translation and Technology.
8. Takeda, K., & Uchimoto, K. (2019). Recent Advances and Future Directions in Automatic Simultaneous Interpreting. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2, 4175-4181.

9. UNESCO Report, (2019). Preliminary Study on the Ethics of Artifical Intelligence. https://unesdoc.unesco.org/ark:/48223/pf0000367823
10. Zou, B., & Thomas, M. (2021). AI and Computer-Assisted Language Learning: Balancing Sustainable Development with Ethics.