Mozhgan Ghassemiazghandi
Biographic Data
| ID | 9239793 |
|---|---|
| NAME | Mozhgan Ghassemiazghandi |
| GIVEN NAMES | Mozhgan |
| FAMILY NAME | Ghassemiazghandi |
| SIGNATURE | GHASSEMIAZGHANDI M |
| AFFILIATIONS | Hospital Universiti Sains Malaysia |
| ORCID | 0000-0002-3038-3124 |
| VERIFIED | Yes |
| TOTAL WORKS | 9 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 9 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Evaluating Large Language Models and Neural Machine Translation Systems in Translating Liaozhaizhiyi
Neural machine translation has demonstrated strong performance in high-resource languages and commercial translation contexts. However, its effectiveness in translating classical Chinese literature remains insufficiently examined. This study conducts a comparative evaluation of three translation systems—ChatGPT, Google Translate, and Youdao Translate—using selected texts from Liaozhaizhiyi as the research corpus. The analysis focuses on four dime…
A DQF-MQM Evaluation of Machine Translation in English–Arabic Customs Law
This study presents a corpus-based diagnostic evaluation of Google Translate’s performance in translating the Revised Kyoto Convention (RKC) from English into Arabic. Building on the "Adequacy-Fluency Tradeoff" observed in recent literature, researchers employ an explanatory sequential triangulation design to assess whether high-resource NMT models can satisfy the strict compliance requirements of Customs law. The methodology applies the COMET me…
Error analysis of large language model-generated film subtitles using the FAR model
Subtitling has long constrained the global reach of Chinese cinema. This study examines the potential of an open-source large language model (LLM) (in enhancing Chinese-to-English subtitle translation by conducting a comprehensive FAR-model-based error analysis on DeepSeek-V3-generated subtitles, focusing on error distribution and underlying linguistic and technical challenges. Using 2028 utterances from the Chinese film YOLO, two expert annotato…
A BLEU-Based Evaluation of ChatGPT's Chinese-to-English Translation
Political text translation presents unique challenges requiring precise ideological expression, cultural sensitivity, and terminological consistency—aspects that extend beyond conventional linguistic accuracy. While ChatGPT demonstrates growing capabilities in machine translation tasks, its performance in specialized political discourse remains underexplored. This study evaluates ChatGPT's Chinese-to-English translation quality using the 2023 Chi…
Quality Assessment in Interlingual Subtitling
This study is a systematic review of the literature on quality assessment in interlingual subtitling, a critical aspect of audiovisual translation, and media accessibility. Subtitling plays a vital role in bridging linguistic and cultural gaps; nevertheless, defining and assessing its quality optimally remains a complex and multifaceted task that is influenced and hindered by diverse stakeholders and parameters. While existing narrative reviews o…
Subtitling Preferences of Film Audiences. A Cross-cultural Survey
Despite growing interest in subtitling, research on how audiences from diverse linguistic and cultural backgrounds perceive and prefer subtitles remains limited. This cross-cultural study explores subtitling preferences among film audiences in Iran, Afghanistan, Malaysia, China, Hungary, and India. Drawing on established subtitling standards, we developed a questionnaire completed by 297 respondents to examine preferences for subtitle format, tra…
Post-editing challenges in Chinese-to-English neural machine translation of movie subtitles
Subtitle translation has been a longstanding factor hindering the overseas development of Chinese movies. The potential of using Neural Machine Translation (NMT) as an innovative solution has yet to be studied. This case study aims to integrate Google Neural Machine Translation (GNMT) into the Chinese-into-English subtitle translation of Chinese movies. The research identifies errors in GNMT-generated subtitles per Pedersen's FAR model and develo…
An Evaluation of ChatGPT's Translation Accuracy Using BLEU Score
Traditional views have long held that machine translation cannot achieve the quality and accuracy of human translators, especially in complex language pairs like Persian and English. This study challenges this perspective by demonstrating that ChatGPT-4, with access to vast amounts of multilingual data and leveraging advanced large language model algorithms, significantly outperforms widely utilized open-source machine translation tools and appro…
Evaluation of Instagram's Neural Machine Translation for Literary Texts
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 at…
No prominent works on this page.
Post-editing challenges in Chinese-to-English neural machine translation of movie subtitles
Subtitle translation has been a longstanding factor hindering the overseas development of Chinese movies. The potential of using Neural Machine Translation (NMT) as an innovative solution has yet to be studied. This case study aims to integrate Google Neural Machine Translation (GNMT) into the Chinese-into-English subtitle translation of Chinese movies. The research identifies errors in GNMT-generated subtitles per Pedersen's FAR model and develo…
An Evaluation of ChatGPT's Translation Accuracy Using BLEU Score
Traditional views have long held that machine translation cannot achieve the quality and accuracy of human translators, especially in complex language pairs like Persian and English. This study challenges this perspective by demonstrating that ChatGPT-4, with access to vast amounts of multilingual data and leveraging advanced large language model algorithms, significantly outperforms widely utilized open-source machine translation tools and appro…
Evaluation of Instagram's Neural Machine Translation for Literary Texts
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 at…
A BLEU-Based Evaluation of ChatGPT's Chinese-to-English Translation
Political text translation presents unique challenges requiring precise ideological expression, cultural sensitivity, and terminological consistency—aspects that extend beyond conventional linguistic accuracy. While ChatGPT demonstrates growing capabilities in machine translation tasks, its performance in specialized political discourse remains underexplored. This study evaluates ChatGPT's Chinese-to-English translation quality using the 2023 Chi…
Quality Assessment in Interlingual Subtitling
This study is a systematic review of the literature on quality assessment in interlingual subtitling, a critical aspect of audiovisual translation, and media accessibility. Subtitling plays a vital role in bridging linguistic and cultural gaps; nevertheless, defining and assessing its quality optimally remains a complex and multifaceted task that is influenced and hindered by diverse stakeholders and parameters. While existing narrative reviews o…
Subtitling Preferences of Film Audiences. A Cross-cultural Survey
Despite growing interest in subtitling, research on how audiences from diverse linguistic and cultural backgrounds perceive and prefer subtitles remains limited. This cross-cultural study explores subtitling preferences among film audiences in Iran, Afghanistan, Malaysia, China, Hungary, and India. Drawing on established subtitling standards, we developed a questionnaire completed by 297 respondents to examine preferences for subtitle format, tra…
Evaluating Large Language Models and Neural Machine Translation Systems in Translating Liaozhaizhiyi
Neural machine translation has demonstrated strong performance in high-resource languages and commercial translation contexts. However, its effectiveness in translating classical Chinese literature remains insufficiently examined. This study conducts a comparative evaluation of three translation systems—ChatGPT, Google Translate, and Youdao Translate—using selected texts from Liaozhaizhiyi as the research corpus. The analysis focuses on four dime…
A DQF-MQM Evaluation of Machine Translation in English–Arabic Customs Law
This study presents a corpus-based diagnostic evaluation of Google Translate’s performance in translating the Revised Kyoto Convention (RKC) from English into Arabic. Building on the "Adequacy-Fluency Tradeoff" observed in recent literature, researchers employ an explanatory sequential triangulation design to assess whether high-resource NMT models can satisfy the strict compliance requirements of Customs law. The methodology applies the COMET me…
Error analysis of large language model-generated film subtitles using the FAR model
Subtitling has long constrained the global reach of Chinese cinema. This study examines the potential of an open-source large language model (LLM) (in enhancing Chinese-to-English subtitle translation by conducting a comprehensive FAR-model-based error analysis on DeepSeek-V3-generated subtitles, focusing on error distribution and underlying linguistic and technical challenges. Using 2028 utterances from the Chinese film YOLO, two expert annotato…
Machine translation (5 works) · Translation Studies and Practices (5 works) · Subtitles and Audiovisual Media (4 works) · Artificial Intelligence (3 works) · Artificial Intelligence in Healthcare and Education (3 works) · Chemistry (3 works) · Computer Science (3 works) · Natural language processing (3 works) · Computational and Text Analysis Methods (2 works) · Interpreting and Communication in Healthcare (2 works)