@inproceedings{sharma-etal-2026-blue,
    title = "blue at {S}em{E}val-2026 Task 4: Synergizing Long-Context Reranking with Semantic Similarity for Narrative Alignment",
    author = "Sharma, Krish  and
      Sharma, Lakksh  and
      Singhal, Rhea  and
      Bedi, Jatin",
    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-1.305/",
    pages = "2421--2426",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes the system submitted by team blue for SemEval-2026 Task 4: Narrative Story Similarity and Narrative Representation Learning, with a primary focus on the Pairwise Similarity subtask (Track A). The core challenge of this task lies in identifying deep structural alignments between stories, which is fundamentally hindered by the restricted context windows of standard transformer architecturesthat truncate narratives before reaching critical plot resolutions. To overcome this context bottleneck, we propose a hybrid ensemble architecture designed to capture extended narrative arcs. Our approach synergizes a cross-encoder (Jina Reranker v2), which processes long inputs via a sliding-window strategy over 1,024-token chunks, to evaluate the global ``course of action,'' with a semantic bi-encoder (RoBERTa-Large) to validate local tonal consistency. This dual-stream system achieved a Pearson correlation score of 0.63, demonstrating that processing narrative content beyond the 512-token truncation boundary is strictly necessary for accurate pairwise narrative comparison."
}