AntCritic: Argument Mining for Free-Form and Visually-Rich Financial Comments

The task of argument mining aims to detect all possible argumentative components and identify their relationships automatically. As a thriving field in natural language processing, there has been a large amount of corpus for academic study and application development in argument mining. 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 means non-adjacent clauses or text segments will always be regarded as multiple individual components, thus restricting the generalization ability of models. To this end, we collect and contribute 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, in order to cope with the challenges and difficulties brought by scenario expansion and problem setting modification, we thoroughly explore the fine-grained relation prediction and structure reconstruction scheme for free-form documents and discuss the encoding mechanism for visual styles and layouts. And based on these analyses, 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.