Duluth at SemEval-2026 Task 4: A Hybrid Approach to Narrative Similarity using Bi-Encoder Embeddings with Cross-Encoder Tie breaking using Learned Weights

AbstractWe present a hybrid system for SemEval-2026 Task 4 on Narrative Similarity. Our approach decomposes the stories into four narrative components: theme, plot, emotion, and outcome. Each component is then encoded using a biencoder (all-mpnet-base-v2), and cosine similarities are combined through a learned pairwise ranking model. When similarity scores between candidate stories fall within a small margin of error, a cross-encoder (ms-marcoMiniLM-L-6-v2) is used as a tie-breaker. Our final system achieves 58.5% accuracy on the official test set, placing us at 39th overall. Error analysis reveals that the system struggles with complex themes, multiple protagonists, and contrasting outcomes.