SoloSemantics at SemEval-2026 Task 4: Triplet-Tuned MPNet for Story Similarity

AbstractThis paper describes Team SoloSemantics’ submissions to SemEval-2026 Task 4: Narrative Story Similarity and Narrative Representation Learning. We began with lightweight neuro-symbolic knowledge-graph baselines, but a triplet-tuned MPNet bi-encoder produced stronger semantic separation in our experiments. We adopted a shared dense encoder family across both tracks and kept the KG and fusion variants as diagnostic baselines. Team SoloSemantics ranked 22nd on Track A and 9th on Track B. Our reproducibility audit further shows that the KG branch was often too sparse on short summaries to represent abstract narrative relations reliably under the current extraction pipeline.