COMPARE：同行评议中比较讨论的分类和数据集
COMPARE: A Taxonomy and Dataset of Comparison Discussions in Peer Reviews
摘要：
比较研究论文是展示实验研究进展的传统方法。我们提出了COMPARE，一个分类和比较讨论的数据集，在实验深度学习领域的研究论文的同行评审。通过对大量评论句的深入观察，我们建立了比较讨论中的类别分类，并提出了一个详细的注释方案来分析这一点。总体而言，我们注释了117条评论，涵盖1，800句。我们尝试了各种方法来识别同行评论中的比较句子，并报告最大F1得分为0.49。我们还专门针对ML，NLP和CV论文摘要和评论预训练了两个语言模型，以学习同行评审的信息表示。注释数据集和预训练模型可在https://github.com/shruti-singh/COMPARE上获得。
英文摘要：
Comparing research papers is a conventional method to demonstrate progress in experimental research. We present COMPARE, a taxonomy and a dataset of comparison discussions in peer reviews of research papers in the domain of experimental deep learning. From a thorough observation of a large set of review sentences, we build a taxonomy of categories in comparison discussions and present a detailed annotation scheme to analyze this. Overall, we annotate 117 reviews covering 1,800 sentences. We experiment with various methods to identify comparison sentences in peer reviews and report a maximum F1 Score of 0.49. We also pretrain two language models specifically on ML, NLP, and CV paper abstracts and reviews to learn informative representations of peer reviews. The annotated dataset and the pretrained models are available at https://github.com/shruti-singh/COMPARE .
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。
