AWEJ for Translation & Literary Studies, Volume 7, Number 1. February  2023                                Pp.220-232

Machine Translation of Selected Ghazals of Hafiz from Persian into English

School of Languages, Literacies and Translation
Universiti Sains Malaysia
Penang, Malaysia


Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation with the potential to overcome many weaknesses in conventional phrase-based translation systems. However, there are still grey areas that need attention in the field of machine translation, mainly in the context of literary translation and poetic language. This research aims to identify the shortcomings of the NMT in addressing ambiguity and the semantic complexities of poetic expressions that are important in translating literature in a cross-cultural and multilingual context. Furthermore, while human translators struggle to retain naturalness and accuracy in translation, this research aims to identify to what extent machine translation could convey the message in translating literary-based texts from Persian into English. The corpus of this study consists of Forty ghazals from the Divan of Hafiz, which is translated by a computer-assisted translation software, memoQ and then compared to the authentic translation of Hafiz by Henry Wilberforce Clarke. This comparative descriptive study portrayed the challenges and issues related to Hafiz’s poems, which demonstrate that the literary touch is lost in MT products. Keywords: Ghazals of Hafiz, Literary Text, Literature, Literary Machine Translation, Machine Translation, Neural
Machine Translation, Persian poetry, Translation, Translation Persian to English

Cite as:

Ghassemiazghandi, M.  (2023). Machine Translation of Selected Ghazals of Hafiz from Persian into English.
Arab World English Journal for Translation & Literary Studies 7 (1): 220-232.
DOI: http://dx.doi.org/10.24093/awejtls/vol7no1.17


Alabau, V., Sanchis, A., & Casacuberta, F. (2014). Improving on-line handwritten recognition in interactive machine translation. Pattern Recognition47(3), 1217-1228.

Allen, J. (2001). Postediting: an integrated part of a translation software program. Language International13(2), 26-29.

Arberry, A. (1993). Discourses of Rumi (OR Fihi Ma Fihi). Trans. AJ Arberry and Samuel Weisner (New York: 1977).

Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.

Banerjee, S., & Lavie, A. (2005, June). METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization (pp. 65-72).

Barrachina, S., Bender, O., Casacuberta, F., Civera, J., Cubel, E., Khadivi, S., … & Vilar, J. M. (2009). Statistical approaches to computer-assisted translation. Computational Linguistics35(1), 3-28.

Bentivogli, L., Bisazza, A., Cettolo, M., & Federico, M. (2016). Neural versus phrase-based machine translation quality: a case study. arXiv preprint arXiv:1608.04631.

Brown, P. F., Della Pietra, S. A., Della Pietra, V. J., & Mercer, R. L. (1993). The mathematics of statistical machine translation: Parameter estimation.

Bywood, L., Georgakopoulou, P., & Etchegoyhen, T. (2017). Embracing the threat: machine translation as a solution for subtitling. Perspectives25(3), 492-508.

Castilho, S., Gaspari, F., Moorkens, J., Popović, M., & Toral, A. (2019). Editors’ foreword to the special issue on human factors in neural machine translation. Machine Translation33(1), 1-7.

Doddington, G. (2002, March). Automatic evaluation of machine translation quality using n-gram co-occurrence statistics. In Proceedings of the second international conference on Human Language Technology Research (pp. 138-145).

Frederking, R., & Nirenburg, S. (1994, October). Three heads are better than one. In Fourth Conference on Applied Natural Language Processing (pp. 95-100).

González-Rubio, J., & Casacuberta, F. (2014). Cost-sensitive active learning for computer-assisted translation. Pattern Recognition Letters37, 124-134.

Jean, S., Firat, O., Cho, K., Memisevic, R., & Bengio, Y. (2015, September). Montreal neural machine translation systems for WMT’15. In Proceedings of the tenth workshop on statistical machine translation (pp. 134-140).

Knight, K., & Chander, I. (1994, July). Automated postediting of documents. In AAAI (Vol. 94, pp. 779-784).

Krings, H. (2001). Repairing Texts [edited by GS Koby, translated from German by GS Koby, GM Shreve, K. Mischerikow and S. Litzer]. Kent, Ohio: Kent State University Press. (1994 post-doctoral thesis).

Krings, H. P., & Koby, G. S. (2001). Repairing texts. Empirical Investigations of Machine Translation Post-Editing Processes. Kent, Ohio: Kent State UP

Luong, T., Sutskever, I., Le, Q., Vinyals, O., & Zaremba, W. (2015). Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. In Addressing the Rare Word Problem in Neural Machine Translation.

Melero, M., Oliver, A., & Badia, T. (2006). Automatic multilingual subtitling in the eTITLE project. Proceedings of Translating and the Computer28, 1-18.

Moorkens, J. (2018). What to expect from Neural Machine Translation: a practical in-class translation evaluation exercise. The Interpreter and translator trainer, 12(4), 375-387.

O’Brien, S. (2005). Methodologies for measuring the correlations between post-editing effort and machine translatability. Machine Translation, 19(1), 37-58.

O’Brien, S. (2006). Pauses as indicators of cognitive effort in post-editing machine translating output. Across Languages and Cultures, 7(1), 1-21.

O’Brien, S. (2004). Machine translatability and post-editing effort: How do they relate. In Proceedings of Translating and the Computer 26.

Olah, C. (2015). Understanding LSTM Networks. URL: https://colah. github.io/posts/2015-08-Understanding-LSTMs

Ortiz-Boix, C., & Matamala, A. (2016). Post-editing wildlife documentary films: A new possible scenario?. Journal of Specialised Translation26, 187-210.

Papineni, K., Roukos, S., Ward, T., & Zhu, W. J. (2002, July). Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics (pp. 311-318).

Plitt, M., & Masselot, F. (2010). A productivity test of statistical machine translation post-editing in a typical localisation context. The Prague bulletin of mathematical linguistics93(1), 7-16.

Pozo, A. d., Loenhout, G. v., Walker, A., Georgakopoulou, Y., & Etchegoyhen, T. (2013). SUMAT: An Online Service for Subtitling by Machine Translation. Annual Public Report.

Snover, M., Dorr, B., Schwartz, R., Micciulla, L., & Makhoul, J. (2006, August). A study of translation edit rate with targeted human annotation. In Proceedings of association for machine translation in the Americas (Vol. 200, No. 6).

Sousa, S. C. M. d., Aziz, W., & Specia, L. (2011). Assessing the Post-Editing Effort for Automatic and Semi-Automatic Translations of DVD Subtitles. Paper presented at the Recent Advances in Natural Language Processing, Hissar, Bulgaria.

Sripada, S. G., Reiter, E., & Hawizy, L. (2004). Evaluating an NLG system using post-editing. WEATHER5, 7.

Thrower, J. A. (1996). The Ways of Religion: An Introduction to the Major Traditions. Edited by Roger Eastern. Oxford University Press, second edition1993.£ 17.95. Scottish Journal of Theology49(4), 504-504.

Toral, A., & Sánchez-Cartagena, V. M. (2017). A multifaceted evaluation of neural versus phrase-based machine translation for 9 language directions. arXiv preprint arXiv:1701.02901.

Toral, A., Wieling, M., & Way, A. (2018). Post-editing effort of a novel with statistical and neural machine translation. Frontiers in Digital Humanities5, 9.

Vaswani, A., Shazeer N., Parmar N. & others (2017) Attention Is All You Need,” Proceedings of the 31st International Conference on Neural Information Processing Systems (2017): 6000–6010.

Veale, T., & Way, A. (1997, February). Gaijin: A bootstrapping, template-driven approach to example-based MT. In Proc. of the NeMNLP97.

Volk, M., Sennrich, R., Hardmeier, C., & Tidström, F. (2010). Machine translation of TV subtitles for large scale production. In JEC 2010; November 4th, 2010; Denver, CO, USA (pp. 53-62). Association for Machine Translation in the Americas.

Way, A. (2013). Traditional and emerging use-cases for machine translation. Proceedings of Translating and the Computer35, 12.

Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., … & Dean, J. (2016). Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144.


Mozhgan Ghassemiazghandi holds a Ph.D. in Translation Studies. Currently, she is a senior lecturer at the School of Languages, Literacies and Translation, Universiti Sains Malaysia (USM). Her area of interest includes translation technology, machine translation and audiovisual translation. Mozhgan is also an experienced freelance translator and subtitler with more than a decade experience.
ORCID ID: https://orcid.org/0000-0002-3038-3124