Argumentation Mining on Essays at Multi Scales

Hao Wang, Zhen Huang, Yong Dou, Yu Hong


Abstract
Argumentation mining on essays is a new challenging task in natural language processing, which aims to identify the types and locations of argumentation components. Recent research mainly models the task as a sequence tagging problem and deal with all the argumentation components at word level. However, this task is not scale-independent. Some types of argumentation components which serve as core opinions on essays or paragraphs, are at essay level or paragraph level. Sequence tagging method conducts reasoning by local context words, and fails to effectively mine these components. To this end, we propose a multi-scale argumentation mining model, where we respectively mine different types of argumentation components at corresponding levels. Besides, an effective coarse-to-fine argumentation fusion mechanism is proposed to further improve the performance. We conduct a serial of experiments on the Persuasive Essay dataset (PE2.0). Experimental results indicate that our model outperforms existing models on mining all types of argumentation components.
Anthology ID:
2020.coling-main.478
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5480–5493
Language:
URL:
https://aclanthology.org/2020.coling-main.478
DOI:
10.18653/v1/2020.coling-main.478
Bibkey:
Cite (ACL):
Hao Wang, Zhen Huang, Yong Dou, and Yu Hong. 2020. Argumentation Mining on Essays at Multi Scales. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5480–5493, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Argumentation Mining on Essays at Multi Scales (Wang et al., COLING 2020)
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PDF:
https://preview.aclanthology.org/author-url/2020.coling-main.478.pdf