The Impact of Crowdsourcers’ Education on Crowdsourcing Translation Quality:
Post-Editing Machine Translation Output
Abstract
This study investigates the impact of participants' educational backgrounds on the quality of post-edited machine-translated output in crowdsourced translation tasks. A closed-crowdsourcing model was implemented, utilizing the Telegram platform to engage participants from a specialized translation studies community. The educational degrees of 30 participants were analyzed, revealing a majority with advanced qualifications: 10 held Ph.D. degrees, 12 held Master's degrees, and 8 held Bachelor's degrees in translation studies. The findings suggest that while educational background is a valuable indicator, other factors such as professional experience and specialization may also influence post-editing effectiveness. The study shows that it is possible to collect useful data using crowdsourcing with strict quality control procedures, even with a relatively small sample size. The article makes recommendations for future research directions to investigate the intricate interactions among variables determining translation quality and emphasizes the significance of taking participants' educational levels into account in crowdsourced translation projects. The research also emphasizes the benefits of using a closed crowdsourcing methodology, such as improved quality control, confidentiality, and access to knowledge. This study advances our knowledge of translation quality in crowdsourcing contexts and offers suggestions for improving translation procedures in the future.
Keywords:
Closed-group model, Crowdsourcing translation, Educational background, Machine translation, Post-editingReferences
Aikawa, T., Yamamoto, K., & Isahara, H. (2012). The impact of Crowdsourcing post-editing with the collaborative translation framework. Advances in Natural Language Processing, 1–10.
Anastasiou, D., & Gupta, R. (2011). Comparison of crowdsourcing translation with machine translation. Journal of Information Science, 37(6), 637–659.
Azari, R., Ganjalikhani, M. K., & Amirshoja’i, A. (2018). Legislation for patient information leaflets in Iran: Focus on lay-friendliness. Health Promotion Perspectives, 8(4), 263. https://doi.org/10. 15171/hpp.2018.37
Azari, R., Ganjalikhani, M. K., & Amirshoja’i, A. (2020). Crowdsourcing translation: An experience of a cloud-based messaging app. Iranian Journal of Translation Studies, 17(68), 7–22.
Aziz, W., Castilho, S., & Specia, L. (2012, May). PET: a Tool for Post-editing and Assessing Machine Translation. In LREC (pp. 3982–3987).
Badziński, A. (2018). Collocations, equivalence and untranslatability as selected critical aspects in medical translation. Towards Understanding Medical Translation and Interpreting, Gdańsk: Wydawnictwo Uniwersytetu Gdańskiego, 67–83.
Beeby, A., Fernández, M., Fox, O., Albir, A., Kozlova, I., Kuznik, A., ... & Wimmer, S. (2009). Results of the validation of the PACTE translation competence model: Acceptability and decision making. Across Languages and Cultures, 10(2), 207–230.
Brabham, D. C. (2008). Crowdsourcing as a model for problem solving: An introduction and cases. Convergence, 14(1), 75–90.
Dolmaya, J. M. (2018). Analyzing the Crowdsourcing Model and Its Impact on Public Perceptions of Translation 1. In Non-Professional Translating and Interpreting (pp. 167–191). Routledge.
Dunne, D. (2022). A post-editing approach to machine translation evaluation at the document-level.
DePalma, D. A., & Kelly, N. (2011). Project management for crowdsourced translation. American Translators Association Scholarly Monograph Series, 379–408.
Estellés-Arolas, E., Navarro-Giner, R., & González-Ladrón-de-Guevara, F. (2015). undefined. Advances in Crowdsourcing, 33–48.
Estellés-Arolas, E., & González-Ladrón-de-Guevara, F. (2012). Towards an integrated crowdsourcing definition. Journal of Information Science, 38(2), 189–200.
https://doi.org/10.1177/0165551512437638
Fuente, L. C. (2015). Motivation to collaboration in TED open translation project. International Journal of Web Based Communities, 11(2), 210.
Gao, M., Xu, W., & Callison-Burch, C. (2015). Cost optimization in crowdsourcing translation: Low cost translations made even cheaper. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 705–713).
Gritti, A. (2012). Crowd outsourcing for software localization (Master's thesis, Universitat Politècnica de Catalunya).
Howe, J. (2006). The rise of crowdsourcing. Wired magazine, 14(6), 176–183.
Hurtado Albir, A. (2015). The Acquisition of Translation Competence. Competences, Tasks, and Assessment in Translator Training. Meta, 60(2), 256–280. https://doi.org/10.7202/1032857ar
Jaworski, R., Seljan, S., & Dunđer, I. (2017, November). Towards educating and motivating the crowd–a crowdsourcing platform for harvesting the fruits of NLP students' labour. In Proc. 8th Language & Technology Conference–Human Language Technologies as a Challenge for Computer Science and Linguistics (pp. 332–336).
Jiménez-Crespo, M. A. (2013). Crowdsourcing, corpus use, and the search for translation naturalness. Translation and Interpreting Studies, 8(1), 23–49.
Jiménez-Crespo, M. (2016). Mobile Apps and Translation Crowdsourcing: The Next Frontier in the Evolution of Translation. Tradumàtica: Tecnologies De La Traducció, 75–84.
Jiménez-Crespo, M. A. (2017). Crowdsourcing and online collaborative translations: Expanding the limits of translation studies. John Benjamins.
Jiménez-Crespo, M. A. (2018). Crowdsourcing and translation quality: Novel approaches in the language industry and translation studies. In Translation quality assessment (pp. 69–93). Springer, Cham.
Khalilizadeh Ganjalikhani, M., Hesabi, A., & Ketabi, S. (2024). Evaluating linguistic comprehensibility of Persian healthcare translations in multilingual contexts: A case study of health translations website from the Victorian Government of Australia. International Journal of Multilingualism, 21(4), 2307–2324. https://doi.org/10.1080/14790718.2023.2236150
Khalilizadeh Ganjalikhani, M., Hesabi, A. and Ketabi, S. (2023). Toward Crowdsourcing Translation Post-editing: A Thematic Systematic Review. Iranian Journal of Applied Language Studies, 15(2), 1–18. doi: 10.22111/ijals.2023.46015.2358
Lommel, A. (2018). Metrics for translation quality assessment: A case for standardising error typologies. In Translation quality assessment: From principles to practice (pp. 109–127). Cham: Springer International Publishing.
Mao, A., Kamar, E., Chen, Y., Horvitz, E., Schwamb, M., Lintott, C., & Smith, A. (2013). Volunteering versus work for pay: Incentives and tradeoffs in crowdsourcing. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing,
Mesipuu, M. (2012). Translation crowdsourcing and user-translator motivation at Facebook and Skype. Translation Spaces, 1, 33–53.
Morera-Mesa, A. (2014). Crowdsourced translation practices from the process flow perspective. PhD diss., University of Limerick.
O’Hagan, M. (2021). Community translation: Translation as a social activity and its possible consequences in the advent of Web 2.0 and beyond. Linguistica Antverpiensia, New Series—Themes in Translation Studies, 10.
Quinci, C. (2015). Translators in the Making: An Empirical Longitudinal Study on Translation Competence and its Development. Università degli studi di Trieste. http://hdl.handle.net/10077/10986
Ranard, B. L., Ha, Y. P., Meisel, Z. F., Asch, D. A., Hill, S. S., Becker, L. B., ... & Merchant, R. M. (2014). Crowdsourcing—harnessing the masses to advance health and medicine, a systematic review. Journal of general internal medicine, 29, 187–203.
Salam, Z. M., Akil, M., & Rahman, A. Q. (2017). Translation errors made by Indonesian-English translators in Crowdsourcing translation application. ELT Worldwide: Journal of English Language Teaching, 4(2), 195.
Sutherlin, G. (2013). A voice in the crowd: Broader implications for crowdsourcing translation during crisis. Journal of Information Science, 39(3), 397–409.
Zhang, M., & Huang, Z. (2022). Crowdsourcing used in higher education: an empirical study on a sustainable translation teaching mode based on crowdsourced translation. Sustainability, 14(6), 3140.
Downloads
Published
How to Cite
Issue
Section
DOR
License
Copyright (c) 2026 Marziyeh Khalilizadeh Ganjalikhani
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright Licensee: Iranian Journal of Translation Studies. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution–NonCommercial 4.0 International (CC BY-NC 4.0 license).
