@inproceedings{lepekhin-sharoff-2025-domain,
    title = "Domain{\_}adaptation at {S}em{E}val-2025 Task 11: Adversarial Domain Adaptation for Text-based Emotion Recognition",
    author = "Lepekhin, Mikhail  and
      Sharoff, Serge",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.semeval-1.8/",
    pages = "49--53",
    ISBN = "979-8-89176-273-2",
    abstract = "We report our participation in the SemEval-2025 shared task on classification of emotions and describe our solutions with BERT-based models and their ensembles. We participate in tracks A and B. We apply and compare base XLM-RoBERTa, Adversarial Domain Adaptation (ADA) on the XLM-RoBERTa with text length as the adversarial feature. As a simple baseline we also use a Logistic Regression based on tf-idf features. We show that the usage of ADA increases the f1 macro score on the low-resource languages, and on the texts of lower length. Besides, we describe our approach to tracks A and C where we use ADA with the text language as the confounder. We show that for some languages it helps to improve the f1 score. In all the tracks we work with the following languages: Russian, Amharic, Algerian Arabic, German, English, Spanish, Hausa, Brasilian Portuguese, Romanian, Ukrainian."
}