识别论证图逻辑重构的隐含前提
Identifying Implicit Premises for Logical Reconstruction of Argument Graphs
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中文总结 AI 辅助
本文针对论证图逻辑重构中省略式论证的隐含前提问题，提出神经符号管道方法，结合大型语言模型生成隐含前提并评估，用于证明陈述间的逻辑关系。
中文摘要 AI 辅助
从自然语言文本对论证图进行逻辑重构颇具挑战性，原因在于省略式论证（即包含隐含前提的论证）普遍存在。目前已有用于识别文本中省略式论证的自然语言处理方法，以及基于溯因推理的符号方法，用于识别省略式论证的逻辑表示中的缺失前提。然而，仍需方法来生成隐含前提，以从逻辑上证明一对陈述之间已知的蕴含或矛盾关系。为解决该问题，本文提出一种神经符号管道，利用大型语言模型（LLMs）生成中间隐含前提，将其转换为逻辑公式，再与表示明确前提和明确主张的逻辑公式结合，以证明它们之间的逻辑关系（蕴含、矛盾或中立）。本文在Microtext Argumentative Corpus上对所提方法进行了评估。
英文摘要
The logical reconstruction of argument graphs from natural language text is challenging because of the prevalence of enthymemes (i.e., arguments with implicit premises). There are natural language processing methods for identifying enthymemes in text, and there are symbolic methods based on abduction for identifying missing premises in a logical representation of enthymemes. However, there is a need for methods to generate implicit premises to logically show a known entailment or contradiction relationship between a pair of statements. To address this, we propose a neuro-symbolic pipeline that uses large language models (LLMs) to generate intermediate implicit premises that are translated into logical formulae and used with logical formulae representing explicit premises and explicit claims to show the logical relationships between them (entailment, contradiction, or neutrality). Our approach is evaluated on the Microtext Argumentative Corpus.
发表机构
- University College London（伦敦大学学院）
机构由 AI 辅助整理，请以论文原文为准。
