Translation process in the biological and biomechanical context: Resources, methods and trends (2013–2023)
Abstract
Over the past decade, translation process research has undergone significant evolution, driven by advancements in cognitive science related to the neural mechanisms underlying language processing in biological systems, technology with applications in biological research translations, and data-driven methodologies considering the unique characteristics of biological data. Recently, there has been an emerging connection between this translation process research and biomechanics. For instance, the cognitive strategies in translation, such as chunking and bilingual control, which are influenced by the brain’s biological architecture involved in language processing, may also be related to the body’s mechanical responses. The neural pathways responsible for these translation strategies could potentially interact with the body’s mechanical systems. In terms of technological tools, neural machine translation (NMT) and computer-assisted translation (CAT) systems, which play a crucial role. in biological translating research papers, could be further optimized by considering biomechanical factors. For example, understanding how the body’s physical movements during long-term translation tasks (like sitting posture and muscle fatigue) affect cognitive performance and translation accuracy. Data resources, including parallel corpora and community-driven initiatives, have become foundational to supporting both high- and low-resource languages. However, integrating these with biomechanical considerations, such as how data collection and use impact the physical well-being of translators, adds a new layer of complexity. Challenges persist in integrating diverse tools, addressing cultural and contextual subtleties, and developing comprehensive evaluation metrics that capture creativity and cultural fidelity. Ethical considerations, such as data privacy and representation of marginalized languages, underscore the need for inclusive and responsible practices. Future research directions focus on context-aware technologies, cross-cultural studies, and hybrid human-machine workflows while also incorporating biomechanical insights to enhance translation quality and cultural sensitivity. By integrating interdisciplinary approaches, including biomechanics, translation process research aims to bridge linguistic divides and foster global understanding.
References
1. Adelani D, et al. The Masakhane project: Building African language resources through community collaboration. Language Resources and Evaluation; 2022.
2. Alabau V, et al. Interactive NMT: Real-time user feedback for adaptive translation. Machine Translation Review; 2020.
3. Albir AH, & Alves F. Empirical research in translation processes: Modeling decision-making and strategies. Language Science Journal. 2015.
4. Alves F, Carl M, & Hurtado Albir A. Challenges in translation process research at the workplace. Translation Studies Quarterly; 2020.
5. Aziz W, et al. Automated quality assurance in translation: Challenges and opportunities. Machine Translation; 2018.
6. Beltagy I, et al. Longformer: The long-document transformer. In: Proceedings of NAACL-HLT; 2020.
7. Bender EM, et al. On the dangers of stochastic parrots: Can language models be too big?. In: Proceedings of FAccT; 2021.
8. Bojar O, et al. WMT shared tasks: Benchmark datasets for translation evaluation. Machine Translation; 2018.
9. Carl M, & Schaeffer M. Keystroke logging and eye-tracking in translation studies. John Benjamins Publishing; 2017.
10. Carl M, Bangalore S, & Schaeffer M. New directions in empirical translation process research. Springer; 2016.
11. Carl M, et al. CRITT Translation Process Research Database (TPR-DB): Expanding data for process analysis. Translation Studies Journal. 2016.
12. Castilho S, et al. Neural machine translation: A step forward in quality evaluation. Machine Translation Review; 2017.
13. De Groot AMB. Bilingualism and neural efficiency: Insights for translation processes. Journal of Cognitive Neuroscience. 2020.
14. Delorme R, et al. Multimodal tools in translation: Augmented reality applications. Translation and Technology Journal. 2020.
15. Di Gangi MA, et al. MuST-C: A multilingual speech translation corpus. In: Proceedings of ACL; 2019.
16. Dragoy O, et al. Neural responses to translation tasks: An EEG study of bilingual professionals. Brain and Language; 2017.
17. Dragsted B, & Carl M. The cognitive impact of CAT tools on translation workflows. Meta; 2019.
18. Dragsted B, et al. Eye-tracking and ambiguity resolution in translation. Translation and Interpreting Studies; 2020.
19. Ehrensberger-Dow M, & Massey G. Ergonomics in translation processes: Exploring the impact of tools and environment. Across Languages and Cultures; 2017.
20. García I, et al. Context-aware neural machine translation for specialized domains. Translation and Localization Journal. 2018.
21. Gaspari F, et al. Ethical issues in cloud-based translation technologies. International Journal of Translation Ethics. 2018.
22. Göpferich S, & Jääskeläinen R. Adaptive expertise in translation: Cognitive and emotional dimensions. Meta: Translators’ Journal. 2020.
23. Guzmán F, et al. Data augmentation for low-resource languages: Back-translation and synthetic data generation. Machine Translation; 2019.
24. Hervais-Adelman A, et al. fMRI evidence of cognitive control during simultaneous interpretation. Cerebral Cortex; 2015.
25. Hovy D, et al. Mining Wikipedia for multilingual parallel corpora. Translation Studies Quarterly; 2018.
26. Hubscher-Davidson S. The role of emotions in translation: From process to product. Translation and Interpreting Studies; 2018.
27. Hvelplund KT. Eye movements and attention distribution in translation. Meta; 2014.
28. Junczys-Dowmunt M., et al. Marian: Fast neural machine translation in C++. In: Proceedings of ACL; 2018.
29. Kiraly DC. Constructivist translator education: Empowering learners with real-world tasks. Meta; 2016.
30. Koehn P, et al. OPUS-MT: Large-scale multilingual machine translation. Journal of Machine Translation Research. 2020.
31. Koglin S, & Neunzig W. Adaptive translation memory systems: A new era of context sensitivity. Target; 2021.
32. Koponen M, et al. AI-powered analysis of translation errors: A data-driven approach. Journal of Translation Studies. 2020.
33. Krüger R. Post-editing machine translation: Cognitive processes and efficiency. Target; 2016.
34. Lacruz I, & Shreve GM. Cognitive load in post-editing neural machine translation: Challenges and strategies. Machine Translation Today; 2020.
35. Malmkjær K. Translation tasks and professional development: Insights from simulation-based training. The Translator; 2020.
36. Maruf S, et al. Context-aware neural machine translation: Bridging sentence boundaries. Computational Linguistics; 2019.
37. Massey G, & Ehrensberger-Dow M. Cognitive apprenticeship in translator training: A constructivist approach. Across Languages and Cultures; 2017.
38. Moorkens J. Ergonomic challenges in technology-assisted translation. Translation Studies Review; 2020.
39. Moorkens J, et al. Perceptions of CAT tools among professional translators. Meta; 2016.
40. Munday J. Literary translation and cultural adaptation: A critical analysis. The Translator; 2020.
41. Muñoz Martín R. Sociocognitive perspectives on translation: Expanding the cognitive boundary. Translation Spaces; 2014.
42. Navigli R, & Ponzetto SP. BabelNet: The multilingual encyclopedic dictionary and semantic network. Artificial Intelligence Journal. 2019.
43. O’Brien S. Human-machine collaboration in translation: A new paradigm. Journal of Translation Studies. 2017.
44. Ott M, et al. Fairseq: A toolkit for sequence-to-sequence learning. In: Proceedings of NAACL-HLT; 2019.
45. Post M, et al. Quality control in crowdsourced translation datasets. Computational Linguistics and Applications; 2018.
46. Qi P, et al. TED Talks corpus for low-resource languages. Language Resources and Evaluation; 2018.
47. Risku H, et al. Decision-making in translation: A socio-cognitive perspective. Meta; 2019.
48. Ruiz C, et al. Working memory and information retrieval in translation. Translation and Cognition Quarterly; 2017.
49. Schwieter JW. & Ferreira A. Bilingual cognitive control and translation processes. Cambridge University Press; 2017.
50. Specia L, et al. Multimodal NMT: Exploring the integration of visual context. Translation Studies Quarterly; 2021.
51. Steurs F, & Kockaert H. Enhancing terminological databases for domain-specific translations. Terminology Science and Research; 2020.
52. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. Advances in Neural Information Processing Systems; 2017.
53. Whyatt B. Cognitive effort in translation: Comparing novice and expert strategies. Language Resources and Evaluation; 2021.
54. Xie L, & Li W. Research on cognitive translation process: An overview. Journal of Translation Studies. 2022.
55. Xie Y, & Li H. Cognitive strategies in translation: A review of recent advancements. Applied Linguistics Review; 2022.
56. Zaidan OF, & Callison-Burch C. Crowdsourcing translation: A resourceful approach to parallel data creation. Computational Linguistics; 2014.
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