Abstract
Climate change is an existential threat to humanity, the proliferation of unsubstantiated claims relating to climate science is manipulating public perception, motivating the need for fact-checking in climate science. In this work, we draw on recent work that uses retrieval-augmented generation for veracity prediction and explanation generation, in framing explanation generation as a query-focused multi-document summarization task. We adapt PRIMERA to the climate science domain by adding additional global attention on claims. Through automatic evaluation and qualitative analysis, we demonstrate that our method is effective at generating explanations.- Anthology ID:
- 2022.alta-1.16
- Volume:
- Proceedings of the 20th Annual Workshop of the Australasian Language Technology Association
- Month:
- December
- Year:
- 2022
- Address:
- Adelaide, Australia
- Editors:
- Pradeesh Parameswaran, Jennifer Biggs, David Powers
- Venue:
- ALTA
- SIG:
- Publisher:
- Australasian Language Technology Association
- Note:
- Pages:
- 122–129
- Language:
- URL:
- https://aclanthology.org/2022.alta-1.16
- DOI:
- Cite (ACL):
- Rui Xing, Shraey Bhatia, Timothy Baldwin, and Jey Han Lau. 2022. Automatic Explanation Generation For Climate Science Claims. In Proceedings of the 20th Annual Workshop of the Australasian Language Technology Association, pages 122–129, Adelaide, Australia. Australasian Language Technology Association.
- Cite (Informal):
- Automatic Explanation Generation For Climate Science Claims (Xing et al., ALTA 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.alta-1.16.pdf