多方对话中议论文质量评价的讨论语料库

Tsukasa Shiota, Kazutaka Shimada
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引用次数: 1

摘要

最近,NLP的许多研究,特别是论证挖掘,都集中在揭示书面文本的好论证是什么。然而,与书面文本相比,由于缺乏语料库,有一些研究对面对面对话中的论证质量评估提出了质疑。为此,我们构建了一个多模态的多方讨论语料库,用于评估多方对话中的论证质量。语料库由10个多方讨论(200分钟)组成。每位参与者都有6分修辞学的注解,修辞学是论证质量的一个等级。在本文中,我们解释了我们的语料库构建的过程,并报告了标注的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Discussion Corpus toward Argumentation Quality Assessment in Multi-Party Conversation
Recently, many studies in NLP, especially argument mining, have focused on revealing what a good argument is with written texts. However, there are a few studies to challenge argumentation quality assessment in face-to-face conversations due to a lack of corpora, as compared with written texts. Therefore, we construct a multimodal multi-party discussion corpus toward argumentation quality assessment in multi-party conversation. The corpus consists of 10 multi-party discussions (200 minutes) in total. Each participant is annotated for 6 scores about rhetoric, which is one of the classes of argumentation quality. In this paper, we explain the procedure of our corpus construction and report the results of the annotation.
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