@inproceedings{kersten-etal-2021-attention,
    title = "Attention vs non-attention for a Shapley-based explanation method",
    author = "Kersten, Tom  and
      Wong, Hugh Mee  and
      Jumelet, Jaap  and
      Hupkes, Dieuwke",
    editor = "Agirre, Eneko  and
      Apidianaki, Marianna  and
      Vuli{\'c}, Ivan",
    booktitle = "Proceedings of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2021.deelio-1.13/",
    doi = "10.18653/v1/2021.deelio-1.13",
    pages = "129--139",
    abstract = "The field of explainable AI has recently seen an explosion in the number of explanation methods for highly non-linear deep neural networks. The extent to which such methods {--} that are often proposed and tested in the domain of computer vision {--} are appropriate to address the explainability challenges in NLP is yet relatively unexplored. In this work, we consider Contextual Decomposition (CD) {--} a Shapley-based input feature attribution method that has been shown to work well for recurrent NLP models {--} and we test the extent to which it is useful for models that contain attention operations. To this end, we extend CD to cover the operations necessary for attention-based models. We then compare how long distance subject-verb relationships are processed by models with and without attention, considering a number of different syntactic structures in two different languages: English and Dutch. Our experiments confirm that CD can successfully be applied for attention-based models as well, providing an alternative Shapley-based attribution method for modern neural networks. In particular, using CD, we show that the English and Dutch models demonstrate similar processing behaviour, but that under the hood there are consistent differences between our attention and non-attention models."
}