Evaluation of Instagram's neural machine translation for literary texts: an MQM-Based Analysis
Addressing the global increase in social media users, platforms such as Instagram introduced automatic translation to broaden information dissemination and improve cross-cultural communication. Yet, the accuracy of these platforms' machine translation systems is still a concern. Therefore...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2024
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| Online Access: | http://journalarticle.ukm.my/23591/1/Gema%20Online_24_1_13.pdf http://journalarticle.ukm.my/23591/ https://ejournal.ukm.my/gema/issue/view/1648 |
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| Institution: | Universiti Kebangsaan Malaysia |
| Language: | English |
| Summary: | Addressing the global increase in social media users, platforms such as Instagram introduced
automatic translation to broaden information dissemination and improve cross-cultural
communication. Yet, the accuracy of these platforms' machine translation systems is still a
concern. Therefore, this paper aims to explore the potential of Neural Machine Translation utilized
by Instagram in producing high-quality translations. In doing so, this study attempts to scrutinize
the reliability of Instagram's "See Translation" feature in the translation of literary texts from
Arabic to English. A selection of auto-translated Instagram captions is analyzed through the
identification, classification, and assignment of error types and penalty points, utilizing the MQM
core typology. Subsequently, the Overall Quality Score of the error-based analysis is calculated
automatically using the ContentQuo platform. Furthermore, the study investigates whether
Instagram Neural Machine Translation can effectively convey the intended message within literary
texts. From 30 purposively selected Instagram captions with literary content, the study found
Instagram's machine translation lacking in 90% of cases, particularly in accuracy, fluency, and
style. Among these, 61 errors were identified: 26 in fluency, 25 in accuracy, and 10 in style,
adversely affecting the quality and failing to convey the original message. The findings suggest a
need for enhanced algorithms and linguistic architecture in Neural Machine Translation systems
to better recognize linguistic variants and text genres for more accurate and fluent translations. |
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