Argument mining aims to detect all possible argumentative components and identify their relationships automatically. As a thriving task in natural language processing, there has been a large amount of corpus for academic study and application development in this field. However, the research in this area is still constrained by the inherent limitations of existing datasets. Specifically, all the publicly available datasets are relatively small in scale, and few of them provide information from other modalities to facilitate the learning process. Moreover, the statements and expressions in these corpora are usually in a compact form, which restricts the generalization ability of models. To this end, we collect a novel dataset AntCritic to serve as a helpful complement to this area, which consists of about 10k free-form and visually-rich financial comments and supports both argument component detection and argument relation prediction tasks. Besides, to cope with the challenges brought by scenario expansion, we thoroughly explore the fine-grained relation prediction and structure reconstruction scheme and discuss the encoding mechanism for visual styles and layouts. On this basis, we design two simple but effective model architectures and conduct various experiments on this dataset to provide benchmark performances as a reference and verify the practicability of our proposed architecture. We release our data and code in this link, and this dataset follows CC BY-NC-ND 4.0 license.

翻译：论辩挖掘旨在自动检测所有可能的论辩成分并识别其相互关系。作为自然语言处理领域蓬勃发展的任务，该领域已有大量用于学术研究和应用开发的语料库。然而，该领域的研究仍受限于现有数据集的内在缺陷。具体而言，所有公开数据集规模相对较小，且极少提供多模态信息以促进学习过程。此外，这些语料库中的陈述与表达通常形式紧凑，限制了模型的泛化能力。为此，我们收集了新型数据集AntCritic作为该领域的有益补充，该数据集包含约1万条自由格式且视觉丰富的金融评论，同时支持论辩成分检测与论辩关系预测任务。此外，为应对场景扩展带来的挑战，我们深入探索了细粒度关系预测与结构重建方案，并讨论了视觉样式与版式的编码机制。在此基础上，我们设计了两种简洁有效的模型架构，在该数据集上进行了多组实验以提供基准性能参考，并验证了所提架构的实用性。我们通过此链接公开数据与代码，本数据集遵循CC BY-NC-ND 4.0许可协议。