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