谁在争论什么？政治辩论中的联合论点-实体检测与分类
Who Argues What? Joint Argument-Entity Detection and Classification in Political Debates
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中文总结 AI 辅助
提出联合论点与实体标注框架，通过微调解码器大模型同时检测辩论中的论点片段和命名实体，在政治辩论数据集上显著提升联合任务性能，并有效泛化至说服性论文领域。
中文摘要 AI 辅助
政治辩论通常通过论点挖掘（Argument Mining, AM）来分析驱动辩论的关键论点。然而，仅凭论证片段往往难以解释政治论点，因为主张和前提通常依赖于它们所提及的实体（例如，人物、事件、地点、政党）。现有的AM资源和方法通常标注论证片段及其角色，但并未提供配对的辩论-实体层来询问辩论中涉及了哪些辩论命名实体（Debate Named Entities, DNE），例如行动者和事件。在本工作中，我们通过以下方式填补这些数据和方法的空白：（i）引入DNE-ElecDeb，一个实体增强版的USElecDeb数据集，在论证性和非论证性片段中均添加DNE，并将辩论命名实体识别（DNER）定义为检测DNE的任务；（ii）提出联合论点与实体标注（Joint Argument and Entity Tagging, JAET），一种生成式框架，微调仅解码器的大型语言模型（LLMs），在保留原始转录的同时，将内联的论点和实体标签插入辩论语句中。在BIO标注评估下，与最强的顺序AM-DNER流水线相比，JAET在联合AM+DNER任务上的相对F1分数在无类型设置下提高了+27.3%，在有类型设置下提高了+41.9%，表明通过组合两个独立模块无法获得这些增益。值得注意的是，类似的提升在说服性论文（Persuasive Essays）上复现（分别+26.6%和+52.7%），显示出对与政治辩论正交领域的有效泛化。通过在单一视图中统一论点和实体级别的表示，我们的贡献为更丰富的政治辩论理解铺平了道路。
英文摘要
Political debates are often analyzed through Argument Mining (AM) to investigate the key arguments that drive them. However, political arguments are rarely interpretable from argumentative spans alone, as claims and premises generally depend on the entities (e.g., people, events, locations, parties) they mention. Existing AM resources and methods typically annotate argumentative spans and roles, but do not provide a paired debate-entity layer for asking which Debate Named Entities (DNE), e.g., actors and events, are invoked within debates. In this work, we address these data and methodological gaps by (i) introducing DNE-ElecDeb, an entity-enriched version of the USElecDeb dataset that adds DNEs in both argumentative and non-argumentative spans and defines Debate Named Entity Recognition (DNER) as the task of detecting DNEs, and (ii) proposing Joint Argument and Entity Tagging (JAET), a generative framework that fine-tunes decoder-only LLMs to insert inline argument and entity tags into debate turns while preserving the original transcript. Under BIO-tagging evaluation, JAET improves relative F1 on the joint AM+DNER task by +27.3%, resp. +41.9%, under the untyped, resp. typed setting over the strongest sequential AM-DNER pipelines, demonstrating that such gains cannot be recovered by composing two independent modules. Notably, similar margins replicate on Persuasive Essays (+26.6%, resp. +52.7%), showing effective generalization to domains orthogonal to political debates. By unifying argumentative and entity-level representations within a single view, our contributions pave the way for richer political debates understanding.
发表机构
- University of Calabria（卡拉布里亚大学）
机构由 AI 辅助整理，请以论文原文为准。
