AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models

AllenNLP Interpret:
A Framework for Explaining Predictions of NLP Models

Eric Wallace, Jens Tuyls, Junlin Wang, Sanjay Subramanian,
Matt Gardner, and Sameer Singh

EMNLP 2019 Demo

Despite constant advances and seemingly super-human performance on constrained domains, state-of-the-art models for NLP are imperfect. These imperfections, coupled with today's advances being driven by (seemingly black-box) neural models, leave researchers and practitioners scratching their heads asking, why did my model make this prediction?

We present AllenNLP Interpret, a toolkit built on top of AllenNLP for interactive model interpretations. The toolkit makes it easy to apply gradient-based saliency maps and adversarial attacks to new models, as well as develop new interpretation methods. AllenNLP interpret contains three components: a suite of interpretation techniques applicable to most models, APIs for developing new interpretation methods (e.g., APIs to obtain input gradients), and reusable front-end components for visualizing the interpretation results.

This page presents links to:

• Paper describing the framework, the technical implementation details, and showing some example use cases.
• Live demos for various models and tasks, such as
• Tutorials for interpreting any model of your choice, and addding a new interpretation method.
• Code for interpreting/attacking models and visualizing the results in the demo (e.g., sentiment analysis).

Citation:

 @inproceedings{Wallace2019AllenNLP, Author = {Eric Wallace and Jens Tuyls and Junlin Wang and Sanjay Subramanian and Matt Gardner and Sameer Singh}, Booktitle = {Empirical Methods in Natural Language Processing}, Year = {2019}, Title = { {AllenNLP Interpret}: A Framework for Explaining Predictions of {NLP} Models}} 

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