Interpretable Neural Predictions with Differentiable Binary Variables
Jasmijn Bastings, Wilker Ferreira Aziz, Ivan S. Titov · 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 rationale (i.e. a short and informative part of the input text), and a classifier that learns from the words in the rationale alone.Previous work proposed to assign binary latent masks to input positions and to promote short selections via sparsityinducing penalties such as L 0 regularisation.We propose a latent model that mixes discrete and continuous behaviour allowing at the same time for binary selections and gradient-based training without REINFORCE.In our formulation, we can tractably compute the expected value of penalties such as L 0 , which allows us to directly optimise the model towards a prespecified text selection rate.We show that our approach is competitive with previous work on rationale extraction, and explore further uses in attention mechanisms.