@inproceedings{aranberri-2026-crowd,
    title = "Crowd-Based Evaluation of Emotion Intensity Preservation in {S}panish{--}{B}asque Tweet Machine Translation",
    author = "Aranberri, Nora",
    editor = "Barnes, Jeremy  and
      Barriere, Valentin  and
      De Clercq, Orph{\'e}e  and
      Klinger, Roman  and
      Nouri, C{\'e}lia  and
      Nozza, Debora  and
      Singh, Pranaydeep",
    booktitle = "The Proceedings for the 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis ({WASSA} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.wassa-1.11/",
    pages = "123--133",
    ISBN = "979-8-89176-378-4",
    abstract = "Machine 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."
}