blue at SemEval-2026 Task 4: Synergizing Long-Context Reranking with Semantic Similarity for Narrative Alignment
Year: 2026
Type: conference-paper
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 architectures that 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.
Institution: Thapar Institute of Engineering & Technology
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166874270