Interpretable Neural Predictions with Differentiable Binary Variables
Meeting of the Association for Computational Linguistics, 2019.
The success of neural networks comes hand in hand with a desire for more interpretability. We focus on text classifiers and make them more interpretable by having them provide a justification, a rationale, for their predictions. We approach this problem by jointly training two neural network models: a latent model that selects a rationa...More
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