@inproceedings{saha-etal-2021-performance,
    title = "Performance of {BERT} on Persuasion for Good",
    author = "Saha, Saumajit  and
      Kalra, Kanika  and
      Patwardhan, Manasi  and
      Karande, Shirish",
    editor = "Bandyopadhyay, Sivaji  and
      Devi, Sobha Lalitha  and
      Bhattacharyya, Pushpak",
    booktitle = "Proceedings of the 18th International Conference on Natural Language Processing (ICON)",
    month = dec,
    year = "2021",
    address = "National Institute of Technology Silchar, Silchar, India",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://aclanthology.org/2021.icon-main.38",
    pages = "313--323",
    abstract = "We consider the task of automatically classifying the persuasion strategy employed by an utterance in a dialog. We base our work on the PERSUASION-FOR-GOOD dataset, which is composed of conversations between crowdworkers trying to convince each other to make donations to a charity. Currently, the best known performance on this dataset, for classification of persuader{'}s strategy, is not derived by employing pretrained language models like BERT. We observe that a straightforward fine-tuning of BERT does not provide significant performance gain. Nevertheless, nonuniformly sampling to account for the class imbalance and a cost function enforcing a hierarchical probabilistic structure on the classes provides an absolute improvement of 10.79{\%} F1 over the previously reported results. On the same dataset, we replicate the framework for classifying the persuadee{'}s response.",
}