As artificial intelligence (AI) continues to evolve, machine translation (MT), particularly neural MT, is experiencing rapid development and innovation, significantly influencing leading MT applications in China. This evolution comes at a pivotal moment when Chinese films are going global at a faster pace, highlighting the importance of effective subtitle translation. However, despite these advancements, there remains a considerable gap in the quality of subtitle translations. The existing two major subtitle translation assessment models—Multidimensional Quality Metrics (MQM) and Functional Equivalence, Acceptability, and Readability (FAR)—face several challenges that hinder their effectiveness. In light of these issues, this study aims to integrate the strengths of both MQM and FAR to develop an enhanced assessment framework, referred to as the FAR 2.0 model. This new model will facilitate a more comprehensive comparison and analysis of four prominent MT applications discussed in this paper, focusing on three critical aspects: functional equivalence (F), acceptance (A), and readability (R). By applying this model, the study seeks to illuminate the respective strengths and weaknesses of each MT tool, offering insights that can guide quality improvements in subtitle translation both now and in the future. Ultimately, this research not only aims to enhance the understanding of current MT capabilities, but also strives to contribute to the broader goal of elevating the global competitiveness of Chinese films through improved subtitle quality.
Primary Language | English |
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Subjects | Translation and Interpretation Studies |
Journal Section | Research Articles |
Authors | |
Publication Date | December 31, 2024 |
Submission Date | October 17, 2024 |
Acceptance Date | December 13, 2024 |
Published in Issue | Year 2024 Volume: 7 Issue: 2 |