@inproceedings(2026-elirf-upv-at-misongyny-iberlef-2026-misogyny-in-song-lyrics,
  author = {Vicent Ahuir Esteve and Sergio Marín Muñoz and María José Castro-Bleda},
  title = {ELiRF-UPV at MisonGyny-IberLEF 2026: Misogyny in Song Lyrics},
  year = {2026},
  booktitle = {Iberian Languages Evaluation Forum},
  address = {León, Spain},
  abstract = {The detection of misogyny in song lyrics introduces unique linguistic and cultural challenges, including implicit bias, genre-specific tropes, and contextual ambiguity. In this working note, we report our participation in the MiSonGyny shared task across all three official tracks. We describe our chunkingbased preprocessing pipeline, domain-adapted model selection, and task-specific training strategies. Our system achieves an overall ranking of 9th-10th place, with Task 1, Task 2, and Task 3 Macro-F1 scores of 0.8334, 0.6562, and 0.7724, respectively. We discuss key technical decisions, failure modes, and cross-task generalization patterns, and outline directions for future work in culturally grounded bias detection.}
)