使用XLNet进行论证元素标注建模
Argumentation Element Annotation Modeling using XLNet
AI总结：
本研究验证了XLNet在议论文论证元素标注中的有效性，其在三个不同标注方案的数据集上表现优异，甚至超过人类一致性，适用于自动化论文反馈。
摘要：
本研究展示了基于Transformer的语言模型XLNet在标注议论文中论证元素方面的有效性。XLNet的架构包含一种循环机制，使其能够对长文本中的长期依赖关系进行建模。经过微调的XLNet模型被应用于三个采用不同标注方案的数据集——一个使用写作修订与反思标注（ARROW）方案的专有数据集、PERSUADE语料库以及论证标注论文（AAE）数据集。XLNet模型在所有数据集上均取得了强劲的性能，在某些情况下甚至超过了人类一致性水平。这表明XLNet能够有效处理多样化的标注方案和长篇论文。对不同数据集上模型输出的比较也揭示了标注标签之间关系的洞见。总体而言，XLNet在跨多样化数据集上对论证结构进行建模的强劲性能，凸显了其在为论文组织结构提供自动化反馈方面的适用性。
英文摘要：
This study demonstrates the effectiveness of XLNet, a transformer-based language model, for annotating argumentative elements in persuasive essays. XLNet's architecture incorporates a recurrent mechanism that allows it to model long-term dependencies in lengthy texts. Fine-tuned XLNet models were applied to three datasets annotated with different schemes - a proprietary dataset using the Annotations for Revisions and Reflections on Writing (ARROW) scheme, the PERSUADE corpus, and the Argument Annotated Essays (AAE) dataset. The XLNet models achieved strong performance across all datasets, even surpassing human agreement levels in some cases. This shows XLNet capably handles diverse annotation schemes and lengthy essays. Comparisons between the model outputs on different datasets also revealed insights into the relationships between the annotation tags. Overall, XLNet's strong performance on modeling argumentative structures across diverse datasets highlights its suitability for providing automated feedback on essay organization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
