Skip to content

ReadMe.md

Latest commit

 

History

History
60 lines (39 loc) · 2.82 KB

ReadMe.md

File metadata and controls

60 lines (39 loc) · 2.82 KB

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:
keane.ongweiyang@u.nus.edu | keaneong@mit.edu

📂 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.

🔖 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}
}