AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models, Hacker News

        

        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-basedsaliency mapsandadversarial attackstonew models, as well as developnew 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:

                 

Paperdescribing the framework, the technical implementation details, and showing some example use cases.

                 

Live demos for various models and tasks, such as         

• Masked Language Modelingusing BERT, to explain why it made certain mask predictions.

            



                                          

• Textual Entailment (andSentiment Analysisusing ELMo-based LSTM classifiers.)             

  

              

• SQuAD and DROPreading comprehensionusing an ELMo-based QANet

                         



           

• NERusing an LSTM-CRF model based on ELMo.

           

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Tutorials for interpreting anymodel of your choice, and adddinga new interpretation method.

        

Codefor interpreting / attacking models and visualizing the results in the demo (eg,sentiment analysis).

                 

Citation:        

@inproceedings {Wallace 2019 AllenNLP,   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}}