Towards Robust ESG Analysis Against Greenwashing Risks: Aspect-Action Analysis with Cross-Category Generalization

📢 Our paper has been accepted to ACL 2025 Main Conference! 🎉

This repository contains the dataset, code, and evaluation scripts for our ACL 2025 paper:

Towards Robust ESG Analysis Against Greenwashing Risks: Aspect-Action Analysis with Cross-Category Generalization
Keane Ong, Rui Mao, Deeksha Varshney, Erik Cambria, Gianmarco Mengaldo

📄 Paper: Read it on ACL Anthology

📬 Contact: Please feel free to reach out if you have any questions or would like to connect:
[email protected] | [email protected]

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📂 Dataset Structure

The datasets used in this work are located in the dataset directory, which includes the following subfolders:

• full/
  Contains the full dataset, with all data from folds 1, 2, and 3 combined.

  • This version does not partition aspect categories into seen and unseen.
  • This is primarily utilised within the paper to test for the general training stability of the dataset.

• fold_1/, fold_2/, fold_3/
  Each fold directory contains four pre-partitioned JSON files based on aspect category visibility:

  • fold_<x>_seen_train.json: Training data with seen aspect categories
  • fold_<x>_seen_val.json: Validation data with seen aspect categories
  • fold_<x>_seen_test.json: Test data with seen aspect categories
  • fold_<x>_unseen_test.json: Test data with unseen aspect categories (i.e., categories not encountered during training)

  (Replace <x> with 1, 2, or 3 for each fold)

  statistics/ directory within each fold provides the details of the data within each fold and its corresponding partitions, including the aspect category count etc.

🧪 Experimental Protocol

• We train on fold_<x>_seen_train.json and validate on fold_<x>_seen_val.json.
• We evaluate model performance on both fold_<x>_seen_test.json and fold_<x>_unseen_test.json.
• The seen partitions include only aspect categories observed during training, while unseen test sets evaluate generalization to novel aspect categories.

📊 Model Evaluation

🚧 Coming soon! We're currently cleaning up our code base and will release model checkpoints and evaluation outputs shortly.

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🔖 Citation

If you find this work useful, please use the following citation:

@article{ong2025greeenwash,
  title={Towards Robust ESG Analysis Against Greenwashing Risks: Aspect-Action Analysis with Cross-Category Generalization},
  author={Ong, Keane and Mao, Rui and Varshney, Deeksha and Cambria, Erik and Mengaldo, Gianmarco},
  journal={arXiv preprint arXiv:2502.15821},
  year={2025}
}