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}} from Hacker News https://ift.tt/30kWbN2