Promoting Human-centred Machine Translation Quality Assessment in NLP education
João Lucas Cavalheiro Camargo · 2023
This research advocates for the integration of Human Evaluation (HE) in Machine Translation Quality Assessment (MTQA), countering the over-reliance on Automatic Evaluation Metrics (AEM) and their associated risks. Highlighting the limitations of AEMs and stressing the strength of HE, this project proposes a mixed-methods training approach for Natural Language Processing (NLP) students, using the ADDIE framework. It aims to equip NLP students with robust HE approaches for a more comprehensive MTQA process, so future developers have the skills to ensure the reliability of MT systems and preventing risks and biases to be propagated.