论文标题
IAM：用于集成参数挖掘任务的全面大规模数据集
IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks
论文作者
论文摘要
传统上，辩论通常需要手动准备过程，包括阅读大量文章，选择索赔，确定索赔的立场，寻求索赔的证据，等等。随着AI辩论这些年来吸引更多的关注，值得探索探索涉及辩论系统繁琐过程的方法。在这项工作中，我们介绍了一个名为IAM的全面且大的数据集，可以应用于一系列参数挖掘任务，包括主张提取，立场分类，证据提取等。我们的数据集从与123个主题相关的1K文章中收集。数据集中的接近70k句子是根据其论点属性（例如索赔，立场，证据等）完全注释的。我们进一步提出了两个与辩论准备过程相关的新的集成参数挖掘任务：（1）使用立场分类（CESC）和（2）索赔 - 证据对提取（CEPE）提取索赔。我们为每个集成任务分别采用管道方法和端到端方法。据报道，有希望的实验结果显示了我们提议的任务的价值和挑战，并激发了未来关于论证挖掘的研究。
Traditionally, a debate usually requires a manual preparation process, including reading plenty of articles, selecting the claims, identifying the stances of the claims, seeking the evidence for the claims, etc. As the AI debate attracts more attention these years, it is worth exploring the methods to automate the tedious process involved in the debating system. In this work, we introduce a comprehensive and large dataset named IAM, which can be applied to a series of argument mining tasks, including claim extraction, stance classification, evidence extraction, etc. Our dataset is collected from over 1k articles related to 123 topics. Near 70k sentences in the dataset are fully annotated based on their argument properties (e.g., claims, stances, evidence, etc.). We further propose two new integrated argument mining tasks associated with the debate preparation process: (1) claim extraction with stance classification (CESC) and (2) claim-evidence pair extraction (CEPE). We adopt a pipeline approach and an end-to-end method for each integrated task separately. Promising experimental results are reported to show the values and challenges of our proposed tasks, and motivate future research on argument mining.
