EnClaim: A Style Augmented Transformer Architecture for Environmental Claim Detection

Diya Saha, Manjira Sinha, Tirthankar Dasgupta


Abstract
Across countries, a noteworthy paradigm shift towards a more sustainable and environmentally responsible economy is underway. However, this positive transition is accompanied by an upsurge in greenwashing, where companies make exaggerated claims about their environmental commitments. To address this challenge and protect consumers, initiatives have emerged to substantiate green claims. With the proliferation of environmental and scientific assertions, a critical need arises for automated methods to detect and validate these claims at scale. In this paper, we introduce EnClaim, a transformer network architecture augmented with stylistic features for automatically detecting claims from open web documents or social media posts. The proposed model considers various linguistic stylistic features in conjunction with language models to predict whether a given statement constitutes a claim. We have rigorously evaluated the model using multiple open datasets. Our initial findings indicate that incorporating stylistic vectors alongside the BERT-based language model enhances the overall effectiveness of environmental claim detection.
Anthology ID:
2024.climatenlp-1.9
Volume:
Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Dominik Stammbach, Jingwei Ni, Tobias Schimanski, Kalyan Dutia, Alok Singh, Julia Bingler, Christophe Christiaen, Neetu Kushwaha, Veruska Muccione, Saeid A. Vaghefi, Markus Leippold
Venues:
ClimateNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
123–132
Language:
URL:
https://aclanthology.org/2024.climatenlp-1.9
DOI:
10.18653/v1/2024.climatenlp-1.9
Bibkey:
Cite (ACL):
Diya Saha, Manjira Sinha, and Tirthankar Dasgupta. 2024. EnClaim: A Style Augmented Transformer Architecture for Environmental Claim Detection. In Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024), pages 123–132, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
EnClaim: A Style Augmented Transformer Architecture for Environmental Claim Detection (Saha et al., ClimateNLP-WS 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.climatenlp-1.9.pdf