arXiv cs.CL· Martial Pastor, Nelleke Oostdijk·· 13 小时前AI 评分31
争议政治话语中省略三段论检测的解释差异与话语结构
Interpretive Variation and Discourse Structure in Detecting Enthymemes in Controversial Political Discourse
AI 导读
针对疫苗接种与移民推文（每条由五名标注者标注），所有微调模型无论数据规模、附加特征或标签类型，F1 均停滞在 0.67 左右。研究提出将预测结果与标注者投票的完整分布进行对比的评估指标，并给出一个直接预测该分布的模型，在不损失 F1 的前提下更好地捕捉标注分歧。模型和话语解析器都依赖显式论证结构，难以识别链接隐含、需读者自行推断的论证。
正文 · 原文
Abstract:Enthymemes are arguments that leave a premise or conclusion unstated, and they are a common persuasion device on social media. Because readers must fill in the missing part themselves, they often disagree on whether a text contains one. This paper asks how to detect and evaluate enthymemes when such disagreement is part of the task. On tweets about vaccination and immigration, each labeled by five annotators, F1 stalls around 0.67 for every fine-tuned model we train, regardless of data size, added features or label type. We therefore ask how these models should be evaluated instead, and propose metrics that compare predictions with the full distribution of annotator votes, together with a model that predicts this distribution directly and captures disagreement better at no cost in F1. We then ask what role discourse structure plays. Models learn to rely on explicit argumentative structure: they reliably recognize tweets without an enthymeme but rarely commit on tweets that contain one. Both the models and a discourse parser miss arguments whose links are left implicit and must be inferred by the reader. Finally, we discuss what these blind spots mean for content moderation systems trained on majority labels.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.30987 [cs.CL] |
| (or arXiv:2610.30987v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.30987 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Martial Pastor [view email]
[v1]
Thu, 22 Oct 2026 09:34:07 UTC (2,757 KB)
来源：arXiv cs.CL · arxiv.org