@inproceedings{benlahbib-etal-2026-nlp,
    title = "{NLP}-{FSDM} at {S}em{E}val-2026 Task 2: Temporal Smoothing and {CCC}-{MAE} Optimization for Balanced Longitudinal Affect Assessment",
    author = "Benlahbib, Abdessamad  and
      Essalmani, Zouhir  and
      Boumhidi, Achraf  and
      Fahfouh, Anass  and
      Alami, Hamza",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.49/",
    pages = "333--337",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes the NLP-FSDM system for SemEval-2026 Task 2, Subtask 1 on longitudinal affect assessment. The task requires predicting Valence and Arousal (V {\&} A) scores for sequences of ecological essays and feeling words written over time. We adopt ModernBERT-large as a text encoder and formulate the task as a joint regression problem optimized using a Concordance Correlation Coefficient (CCC) loss combined with a lightly weighted Mean Absolute Error (MAE) term. To reduce variance induced by fine-tuning large transformers on relatively small user-specific datasets, we employ a three-seed ensemble. Finally, we introduce a lightweight post-inference temporal smoothing mechanism applied per user to improve within-user consistency. Our system achieves an rcomposite of 0.546 for Valence and 0.453 for Arousal, demonstrating stable cross-dimensional performance without explicitly modeling sequential dependencies."
}