Crowd-Based Evaluation of Emotion Intensity Preservation in Spanish–Basque Tweet Machine Translation

AbstractMachine translation (MT) systems perform well on standard benchmarks, yet their ability to preserve emotional meaning in informal user-generated content—particularly for low-resource languages—remains underexplored. We investigate the preservation of emotion intensity in Spanish–Basque tweet translation, focusing on Basque, an under-represented language in MT research. We compile a small, controlled corpus of Spanish reaction tweets and evaluate Basque translations from three publicly available systems through a crowd-based study. While all systems achieve comparable and above mid-range accuracy and fluency, emotion intensity is systematically attenuated in the translations, with greater loss for more emotionally intense inputs. A follow-up on highly emotional tweets shows that LLM prompting reduces emotion loss, yet substantial attenuation remains, highlighting emotion preservation as a persistent challenge in Spanish–Basque MT.