用结构门控机制分离故事的因果骨架与表面词汇，提升叙事相似性判断能力。
Lacuna Inc. at SemEval-2026 Task 4: Structurally Gated State-Space Models for Disentangling Narrative Similarity
- 基于混合状态空间模型，设计分层门控架构捕捉故事结构
- 在双赛道任务中超越基线，显著降低表面词干扰影响
- 适合需要深层叙事理解的文本分析场景
本文介绍我们提交至 SemEval-2026 任务4（叙事相似性与表征学习）的不变-可变解耦状态空间模型（IVD-SSM）。评估叙事相似性需超越具体人物、物品或场景等表面元素，识别抽象的因果链与情节发展模式。为避免标准 Transformer 的二次复杂度瓶颈，我们采用 Jamba-1.5-Mini 混合状态空间模型作为基础。在此基础上，提出结构门控对齐（SGA）头，一种可微分的算法架构：通过大幅下采样的宏观路径提取故事粗略结构骨架，再以此作为门控信号过滤全分辨率微观路径，主动抑制语义噪声和表面关键词重叠。在成对比较判断（Track A）与密集表征学习（Track B）上均验证了该方法的有效性，表明显式解耦结构不变量与词汇可变量能提供稳健、有原则的深层叙事理解框架。
原文摘要 · Abstract (English)
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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