We interpret the predictions of any black-box structured input-structured output model around a specific input-output pair. Our method returns an "explanation" consisting of groups of input-output tokens that are causally related. These dependencies are inferred by querying the black-box model with perturbed inputs, generating a graph over tokens from the responses, and solving a partitioning problem to select the most relevant components. We focus the general approach on sequence-to-sequence problems, adopting a variational autoencoder to yield meaningful input perturbations. We test our method across several NLP sequence generation tasks.
@article{arxiv.1707.01943,
title = {A causal framework for explaining the predictions of black-box sequence-to-sequence models},
author = {David Alvarez-Melis and Tommi S. Jaakkola},
journal= {arXiv preprint arXiv:1707.01943},
year = {2017}
}