Abstract
Justification is an explanation that supports the veracity assigned to a claim in fact-checking. However, the task of justification generation has been previously oversimplified as summarization of a fact-check article authored by fact-checkers. Therefore, we propose a realistic approach to generate justification based on retrieved evidence. We present a new benchmark dataset called ExClaim (for Explainable fact-checking of real-world Claims), and introduce JustiLM, a novel few-shot Justification generation based on retrieval-augmented Language Model by using fact-check articles as an auxiliary resource during training only. Experiments show that JustiLM achieves promising performance in justification generation compared to strong baselines, and can also enhance veracity classification with a straightforward extension.1 Code and dataset are released at https://github.com/znhy1024/JustiLM.- Anthology ID:
- 2024.tacl-1.19
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 12
- Month:
- Year:
- 2024
- Address:
- Cambridge, MA
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 334–354
- Language:
- URL:
- https://aclanthology.org/2024.tacl-1.19
- DOI:
- 10.1162/tacl_a_00649
- Cite (ACL):
- Fengzhu Zeng and Wei Gao. 2024. JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims. Transactions of the Association for Computational Linguistics, 12:334–354.
- Cite (Informal):
- JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims (Zeng & Gao, TACL 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.tacl-1.19.pdf