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

Huadai Liu, Xu Wenqiang, Xuan Lin, Jingjing Huo, Hong Chen, Zhou Zhao


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 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.
Anthology ID:
2024.lrec-main.117
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
1306–1317
Language:
URL:
https://aclanthology.org/2024.lrec-main.117
DOI:
Bibkey:
Cite (ACL):
Huadai Liu, Xu Wenqiang, Xuan Lin, Jingjing Huo, Hong Chen, and Zhou Zhao. 2024. AntCritic: Argument Mining for Free-Form and Visually-Rich Financial Comments. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 1306–1317, Torino, Italia. ELRA and ICCL.
Cite (Informal):
AntCritic: Argument Mining for Free-Form and Visually-Rich Financial Comments (Liu et al., LREC-COLING 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.lrec-main.117.pdf
Optional supplementary material:
 2024.lrec-main.117.OptionalSupplementaryMaterial.zip