Attention vs non-attention for a Shapley-based explanation method
This work addresses explainability challenges in NLP for researchers and practitioners, but it is incremental as it extends an existing method to a new model type.
The authors tackled the problem of applying a Shapley-based explanation method, Contextual Decomposition (CD), to attention-based NLP models, extending it to cover attention operations and testing it on English and Dutch models. They found that CD can successfully be applied to attention-based models, revealing similar processing behavior between languages but consistent differences between attention and non-attention models.
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.