MQM-Chat: Multidimensional Quality Metrics for Chat Translation
It addresses the need for precise evaluation in chat translation, which is important for improving translation quality in conversational AI, but is incremental as it focuses on a new metric rather than a breakthrough method.
This study tackled the problem of evaluating machine translation for chats by introducing MQM-Chat, a multidimensional quality metric, and found that all tested models made fundamental errors like omissions and buzzword issues, leading to loss of stylized information.
The complexities of chats pose significant challenges for machine translation models. Recognizing the need for a precise evaluation metric to address the issues of chat translation, this study introduces Multidimensional Quality Metrics for Chat Translation (MQM-Chat). Through the experiments of five models using MQM-Chat, we observed that all models generated certain fundamental errors, while each of them has different shortcomings, such as omission, overly correcting ambiguous source content, and buzzword issues, resulting in the loss of stylized information. Our findings underscore the effectiveness of MQM-Chat in evaluating chat translation, emphasizing the importance of stylized content and dialogue consistency for future studies.