@inproceedings{bhargava-etal-2021-generalization,
    title = "Generalization in {NLI}: Ways (Not) To Go Beyond Simple Heuristics",
    author = "Bhargava, Prajjwal  and
      Drozd, Aleksandr  and
      Rogers, Anna",
    editor = "Sedoc, Jo{\~a}o  and
      Rogers, Anna  and
      Rumshisky, Anna  and
      Tafreshi, Shabnam",
    booktitle = "Proceedings of the Second Workshop on Insights from Negative Results in NLP",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.insights-1.18/",
    doi = "10.18653/v1/2021.insights-1.18",
    pages = "125--135",
    abstract = "Much of recent progress in NLU was shown to be due to models' learning dataset-specific heuristics. We conduct a case study of generalization in NLI (from MNLI to the adversarially constructed HANS dataset) in a range of BERT-based architectures (adapters, Siamese Transformers, HEX debiasing), as well as with subsampling the data and increasing the model size. We report 2 successful and 3 unsuccessful strategies, all providing insights into how Transformer-based models learn to generalize."
}