@inproceedings{fu-etal-2026-teleai,
    title = "{T}ele{AI} at {S}em{E}val-2026 Task 4: Few-Shot Narrative Similarity Modeling for Classification and Ranking",
    author = "Fu, Weiwei  and
      Wang, Shiquan  and
      Fang, Ruiyu  and
      Song, Shuangyong",
    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-1.164/",
    pages = "1228--1236",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper presents a unified, task-adaptive modeling framework for the two tracks of SemEval-2026 Task 4: Narrative Similarity. For Track A, we build a three-stage pipeline of three-dimensional narrative-anchored chain-of-thought (CoT) reasoning, multi-view data augmentation, and Low-Rank Adaptation (LoRA) fine-tuning. For Track B, we design an architecture fully aligned with the ranking inference pipeline and task objective, along with corresponding data augmentation and expansion methods, and propose Smooth Cosine Contrastive Loss (SCCL) to stabilize training in low-resource settings. Systematic experiments verify the effectiveness of each core module, and our final systems rank 4th in both tracks, providing a reproducible technical solution for few-shot similarity modeling."
}