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...
| 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/ http://journalarticle.ukm.my/23591/1/Gema%20Online_24_1_13.pdf |
| _version_ | 1848815887898902528 |
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| author | Fakih, Altaf Ghassemiazghandi, Mozhgan Fakih, Abdul-Hafeed Manjet K. M. Singh, |
| author_facet | Fakih, Altaf Ghassemiazghandi, Mozhgan Fakih, Abdul-Hafeed Manjet K. M. Singh, |
| author_sort | Fakih, Altaf |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T00:57:08Z |
| format | Article |
| id | oai:generic.eprints.org:23591 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:57:08Z |
| publishDate | 2024 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:235912024-05-24T01:18:05Z http://journalarticle.ukm.my/23591/ Evaluation of Instagram's neural machine translation for literary texts: an MQM-Based Analysis Fakih, Altaf Ghassemiazghandi, Mozhgan Fakih, Abdul-Hafeed Manjet K. M. Singh, 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. Penerbit Universiti Kebangsaan Malaysia 2024-02 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/23591/1/Gema%20Online_24_1_13.pdf Fakih, Altaf and Ghassemiazghandi, Mozhgan and Fakih, Abdul-Hafeed and Manjet K. M. Singh, (2024) Evaluation of Instagram's neural machine translation for literary texts: an MQM-Based Analysis. GEMA: Online Journal of Language Studies, 24 (1). pp. 213-233. ISSN 1675-8021 https://ejournal.ukm.my/gema/issue/view/1648 |
| spellingShingle | Fakih, Altaf Ghassemiazghandi, Mozhgan Fakih, Abdul-Hafeed Manjet K. M. Singh, Evaluation of Instagram's neural machine translation for literary texts: an MQM-Based Analysis |
| title | Evaluation of Instagram's neural machine translation for literary texts: an MQM-Based Analysis |
| title_full | Evaluation of Instagram's neural machine translation for literary texts: an MQM-Based Analysis |
| title_fullStr | Evaluation of Instagram's neural machine translation for literary texts: an MQM-Based Analysis |
| title_full_unstemmed | Evaluation of Instagram's neural machine translation for literary texts: an MQM-Based Analysis |
| title_short | Evaluation of Instagram's neural machine translation for literary texts: an MQM-Based Analysis |
| title_sort | evaluation of instagram's neural machine translation for literary texts: an mqm-based analysis |
| url | http://journalarticle.ukm.my/23591/ http://journalarticle.ukm.my/23591/ http://journalarticle.ukm.my/23591/1/Gema%20Online_24_1_13.pdf |