The paper titled "Impact of Artificial Intelligence (AI) on Translation Quality: Assessment and Evaluation" primarily focuses on examining the influence of AI on the accuracy and reliability of translation quality assessments. The research seeks to explore how the integration of AI tools impacts the evaluation process, specifically addressing three key aspects: the overall improvement in translation evaluation accuracy due to AI, the role of source text quality in AI-assisted evaluations, and the effect of different AI models on the precision of translation assessments.
The purpose of the study is to investigate the efficacy of AI models in detecting errors and ensuring accuracy in translation quality assessment. The research also aims to identify the factors that influence the precision of these AI models, thereby providing insights into how AI can be leveraged to enhance translation quality.
The methodology involves a quantitative survey approach, collecting data from a sample of 450 participants, including professional translators, postgraduate students in translation studies, and foreign language learners who frequently use AI tools. The survey was administered digitally, and the responses were analyzed using statistical tools such as mean, standard deviation, and t-tests to validate the hypotheses.
Key findings from the study indicate that over 75% of participants believe that AI significantly improves the accuracy of translation quality assessments. The research also reveals that the quality of the source text plays a crucial role in determining the accuracy of AI-assisted evaluations, with over 61% of respondents acknowledging this impact. Additionally, the type of AI model used in the evaluation process significantly affects the precision of translation assessments, as supported by the data.
The conclusions drawn from this study suggest that while AI models are instrumental in enhancing translation quality assessments, the expertise of the evaluators and the quality of the source text remain critical factors. The findings emphasize the importance of selecting appropriate AI models and ensuring high-quality input to achieve accurate and reliable translation evaluations.