Lacuna Inc. at SemEval-2026 Task 4: Structurally Gated State-Space Models for Disentangling Narrative Similarity

P. 2347–2353.

Куделя А. В., Алшауи Р., Shirnin A.

In this paper, we present the Invariant-Variant Disentangled State-Space Model (IVD-SSM),our submission to SemEval-2026 Task 4 on Narrative Story Similarity and Narrative Representation Learning. Evaluating narrative similarity is a profound computational challenge that requires models to look past concrete, superficial elements such as specific names, actors, objects, or settings to isolate and compareabstract patterns of causality and plot progression. To model these extended causal chainswithout the quadratic bottlenecks of standard Transformers, we leverage a hybrid State-SpaceModel (Jamba-1.5-Mini). Building upon this backbone, we introduce the Structurally Gated Alignment (SGA) head, a novel, differentiable algorithmic architecture. The SGA head operates on two scales: a heavily strided Macro-path maps the coarse structural skeleton of a story, which then acts as a gating mechanism to filter a full-resolution Micro-path, actively suppressing semantic noise and superficial keyword overlaps. Evaluated on both pairwisecomparative judgments (Track A) and dense representation learning (Track B), our approach demonstrates that explicitly disentangling structural invariants from lexical variants provides a robust, principled framework for deep narrative understanding.

Keywords: Natural Language Processing (NLP)

Publication based on the results of: Development of mathematical models and methods for natural language processing, knowledge discovery in data and recommender systems (2022)

In book

Proceedings of the 20th International Workshop on Semantic Evaluation (2026)

Association for Computational Linguistics, 2026.