@inproceedings{liu-etal-2024-antcritic,
    title = "{A}nt{C}ritic: Argument Mining for Free-Form and Visually-Rich Financial Comments",
    author = "Liu, Huadai  and
      Wenqiang, Xu  and
      Lin, Xuan  and
      Huo, Jingjing  and
      Chen, Hong  and
      Zhao, Zhou",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.117",
    pages = "1306--1317",
    abstract = "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 \textit{compact} form, which restricts the generalization ability of models. To this end, we collect a novel dataset \textit{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 \textit{link}, and this dataset follows CC BY-NC-ND 4.0 license.",
}

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 \textit{compact} form, which restricts the generalization ability of models. To this end, we collect a novel dataset \textit{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 \textit{link}, and this dataset follows CC BY-NC-ND 4.0 license.
Liu, Huadai Wenqiang, Xu Lin, Xuan Huo, Jingjing Chen, Hong Zhao, Zhou
AntCritic: Argument Mining for Free-Form and Visually-Rich Financial Comments