Study Finds Strong Correlation Between Expert and GPT-3.5 in Interpreting Quality Assessment
In a paper presented at the 2024 conference of the European Association for Machine Translation (EAMT), researchers Xiaoman Wang and Claudio Fantinuoli find a strong correlation between expert evaluations and the use of Open AI's GPT-3.5 in assessing the quality of translated speech.
Correlation Between Automated Metrics and Expert Evaluations
The research conducted by Wang and Fantinuoli explores the correlation between automated metrics and expert evaluations in both human simultaneous interpreting and AI speech translation. The study suggests that the use of large language models (LLMs) aligns positively with human scores across various evaluation methods.
Benefits of AI in Interpreting Quality Assessment
Utilizing artificial intelligence (AI) for assessing interpreting quality could prove to be beneficial for professional interpreters, interpreter trainers, students, and machine speech translation developers as a tool to enhance performance.
Challenges in Interpreting Quality Assessment
Assessing the quality of simultaneous interpreting is a complex task due to the nuances of multilingual communication and the varied strategies employed by interpreters. Despite its challenges, interpreting quality assessment can provide valuable insights for practitioners, educators, scholars, and certification bodies.
Reliability of Automated Metrics
The researchers conducted a preliminary study to investigate the reliability of automated metrics in evaluating simultaneous interpreting. By comparing automatic assessment results with expert-curated evaluations, the study focused on the accuracy of meaning transfer between languages.
Integration of AI in Quality Evaluation
The integration of AI-enabled quality evaluation can offer new resources and perspectives in interpreting. Interpreters, trainer, students, and designers of speech translation systems can benefit from automated quality evaluation for professional development and technology enhancement.
Limitations and Future Research
Despite the advancements in AI technology, there are limitations to using fully-automated approaches for interpreting quality assessment. The study acknowledges the need for further research and expert guidance in utilizing automated evaluation metrics in production.
It is important to note that while AI-assisted evaluation can provide valuable insights, it should not be considered a standalone solution for assessing interpreting quality consistently and objectively.
More research is required before these metrics can be fully integrated into production, especially considering the complex nature of interpreting services and the diverse needs of end users.
As the authors suggest, further studies are necessary to refine and enhance the use of automated evaluation metrics in interpreting quality assessment.
To learn more about the research, you can access the full paper here.
