"Honey, Tell Me What's Wrong", Global Explanation of Textual
  Discriminative Models through Cooperative Generation
Antoine Chaffin Julien Delaunay
The ubiquity of complex machine learning has raised the importance of
model-agnostic explanation algorithms. These methods create artificial
instances by slightly perturbing real instances, capturing shifts in model
decisions. However, such methods rely on initial data and only provide
explanations of the decision for these. To tackle these problems, we propose
Therapy, the first global and model-agnostic explanation method adapted to text
which requires no input dataset. Therapy generates texts following the
distribution learned by a classifier through cooperative generation. Because it
does not rely on initial samples, it allows to generate explanations even when
data is absent (e.g., for confidentiality reasons). Moreover, conversely to
existing methods that combine multiple local explanations into a global one,
Therapy offers a global overview of the model behavior on the input space. Our
experiments show that although using no input data to generate samples, Therapy
provides insightful information about features used by the classifier that is
competitive with the ones from methods relying on input samples and outperforms
them when input samples are not specific to the studied model.