增量叙事理解中修订与延迟阐释的区分
Distinguishing Revision and Delayed Elaboration in Incremental Narrative Interpretation
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中文总结 AI 辅助
研究区分增量叙事理解中的修订与延迟阐释两种更新算子，以视觉叙事为域验证结构化表征可支持二者，其结构差异对增量推理及混合符号-神经系统具重要意义。
中文摘要 AI 辅助
处理叙事或长篇内容的人类与AI系统均以增量方式运行：输入随时间接收，内部表征需相应更新。因此，增量理解不仅依赖所表征的内容，还依赖表征状态在新证据下的演变方式。我们区分了叙事理解中出现的两种结构上不同的更新算子：修订驱动的更新与延迟阐释。修订驱动的更新会因矛盾撤回或替换先前已确定的结构，因此是非单调的；相比之下，延迟阐释通过添加约束来细化初始未明确的元素，且不撤回先前的承诺，从而使理解状态单调扩展。尽管两种算子都可能改变对早期材料的理解方式，但它们对状态转换施加了根本不同的结构要求。我们以视觉叙事作为诊断领域，展示了结构化叙事表征如何能明确区分已确定内容与未明确内容，并在增量构建过程中支持两种更新算子。通过一个示例，我们展示了延迟阐释如何实现理解状态的单调细化，而修订则需要非单调修正。我们讨论了这种结构区分对增量推理及混合符号-神经系统的更广泛意义。
英文摘要
Both human and AI systems that process narrative or long-form content operate incrementally: input is received over time, and internal representations must be updated accordingly. Incremental interpretation, therefore, depends not only on what is represented but also on how the representational state evolves under new evidence. We distinguish two structurally different update operators that arise in narrative interpretation: revision-driven update and delayed elaboration. Revision-driven updates retract or replace previously committed structure in response to a contradiction and are therefore non-monotonic. Delayed elaboration, by contrast, refines initially underspecified elements through constraint addition without retracting prior commitments, yielding monotonic extension of the interpretive state. Although both operators may alter how earlier material is understood, they impose fundamentally different structural requirements on state transitions. Using visual narratives as a diagnostic domain, we demonstrate how a structured narrative representation can explicitly separate committed from underspecified content and support both update operators during incremental construction. Through a worked example, we show how delayed elaboration enables monotonic refinement of interpretive state, while revision requires non-monotonic correction. We discuss the broader relevance of this structural distinction for incremental reasoning and hybrid symbolic-neural systems.
发表机构
- National Cheng Kung University（成功大学）
机构由 AI 辅助整理，请以论文原文为准。
