你为什么不与之相比？确定用作基线的文件
Why Did You Not Compare With That? Identifying Papers for Use as Baselines
摘要：
我们提出了自动识别科学文章中用作基线的论文的任务。我们将问题框架为二元分类任务，其中论文中的所有参考文献都被分类为基线或非基线。这是一个具有挑战性的问题，因为基线参考可以以多种方式出现在论文中。我们开发了一个数据集的$2,075$论文从ACL选集语料库与所有的参考文献手动注释为两个类之一。我们为基线分类任务开发了一个多模块基于注意力的神经分类器，当应用于基线分类任务时，它的性能优于四种最先进的引用角色分类方法。我们还提出了一个建议的分类器的错误分析，引出的挑战，使基线识别一个具有挑战性的问题。
英文摘要：
We propose the task of automatically identifying papers used as baselines in a scientific article. We frame the problem as a binary classification task where all the references in a paper are to be classified as either baselines or non-baselines. This is a challenging problem due to the numerous ways in which a baseline reference can appear in a paper. We develop a dataset of $2,075$ papers from ACL anthology corpus with all their references manually annotated as one of the two classes. We develop a multi-module attention-based neural classifier for the baseline classification task that outperforms four state-of-the-art citation role classification methods when applied to the baseline classification task. We also present an analysis of the errors made by the proposed classifier, eliciting the challenges that make baseline identification a challenging problem.
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。
