A causal framework for explaining the predictions of black-box sequence-to-sequence models

David Alvarez-Melis; Tommi S. Jaakkola

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arXiv · 1707.01943

A causal framework for explaining the predictions of black-box sequence-to-sequence models

David Alvarez-Melis·Tommi S. Jaakkola

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Abstract

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.

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David Alvarez-Melis, Tommi S. Jaakkola. 2017-07-06. A causal framework for explaining the predictions of black-box sequence-to-sequence models. https://arxiv.org/abs/1707.01943

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