Argumentation-Driven Evidence Association in Criminal Cases

Yefei Teng, WenHan Chao


Abstract
Evidence association in criminal cases is dividing a set of judicial evidence into several non-overlapping subsets, improving the interpretability and legality of conviction. Observably, evidence divided into the same subset usually supports the same claim. Therefore, we propose an argumentation-driven supervised learning method to calculate the distance between evidence pairs for the following evidence association step in this paper. Experimental results on a real-world dataset demonstrate the effectiveness of our method.
Anthology ID:
2021.findings-emnlp.257
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
EMNLP | Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2997–3001
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.257
DOI:
10.18653/v1/2021.findings-emnlp.257
Bibkey:
Cite (ACL):
Yefei Teng and WenHan Chao. 2021. Argumentation-Driven Evidence Association in Criminal Cases. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2997–3001, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Argumentation-Driven Evidence Association in Criminal Cases (Teng & Chao, Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.257.pdf