@inproceedings{huang-etal-2025-promotiongo,
    title = "{P}romotion{G}o at {L}e{W}i{D}i-2025: Enhancing Multilingual Irony Detection with Data-Augmented Ensembles and {L}1 Loss",
    author = "Huang, Ziyi  and
      Abeynayake, N. R.  and
      Cui, Xia",
    editor = "Abercrombie, Gavin  and
      Basile, Valerio  and
      Frenda, Simona  and
      Tonelli, Sara  and
      Dudy, Shiran",
    booktitle = "Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.nlperspectives-1.21/",
    pages = "242--248",
    ISBN = "979-8-89176-350-0",
    abstract = "This paper presents our system for the Learning with Disagreements (LeWiDi-2025) shared task (Leonardelli et al., 2025), which targets the challenges of interpretative variation in multilingual irony detection. We introduce a unified framework that models annotator disagreement through soft-label prediction, multilingual adaptation and robustness-oriented training. Our approach integrates tailored data augmentation strategies (i.e., lexical swaps, prompt-based reformulation and back-translation) with an ensemble learning scheme to enhance sensitivity to contextual and cultural nuances. To better align predictions with human-annotated probability distributions, we compare multiple loss functions, including cross-entropy, Kullback{---}Leibler divergence and L1 loss, the latter showing the strongest compatibility with the Average Manhattan Distance evaluation metric. Comprehensive ablation studies reveal that data augmentation and ensemble learning consistently improve performance across languages, with their combination delivering the largest gains. The results demonstrate the effectiveness of combining augmentation diversity, metric-compatible optimisation and ensemble aggregation for tackling interpretative variation in multilingual irony detection."
}