Lacuna公司在SemEval-2026任务4中的表现：用于解开叙事相似性的结构门控状态空间模型
Lacuna Inc. at SemEval-2026 Task 4: Structurally Gated State-Space Models for Disentangling Narrative Similarity
AI总结：
研究叙事相似性评估难题，用混合状态空间模型避免标准Transformer二次瓶颈，引入结构门控对齐头，在不同任务中验证从词汇变体中分离结构不变量可助力深度叙事理解。
摘要：
本文介绍了不变-变体解缠状态空间模型（IVD-SSM），这是我们提交给SemEval-2026任务4关于叙事故事相似性和叙事表示学习的成果。评估叙事相似性是一项重大计算挑战，我们利用混合状态空间模型并引入结构门控对齐头，在不同任务评估中展示了相关方法的有效性。
英文摘要：
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 compare abstract patterns of causality and plot progression. To model these extended causal chains without the quadratic bottlenecks of standard Transformers, we leverage a hybrid State-Space Model (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 pairwise comparative 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.
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