AllenNLP Interpret: A Framework for Explaining Predictions of NLP Fashions

AllenNLP Interpret: A Framework for Explaining Predictions of NLP Fashions

Regardless of fixed advances and seemingly super-human efficiency on constrained domains, state-of-the-art fashions for NLP are imperfect. These imperfections, coupled with at present’s advances being pushed by (seemingly black-box) neural fashions, depart researchers and practitioners scratching their heads asking, why did my mannequin make this prediction?

We current AllenNLP Interpret, a toolkit constructed on prime of AllenNLP for interactive mannequin interpretations. The toolkit makes it simple to use gradient-based saliency maps and adversarial assaults to new fashions, in addition to develop new interpretation strategies. AllenNLP interpret comprises three elements: a collection of interpretation strategies relevant to most fashions, APIs for growing new interpretation strategies (e.g., APIs to acquire enter gradients), and reusable front-end elements for visualizing the interpretation outcomes.

This web page presents hyperlinks to:

  • Paper describing the framework, the technical implementation particulars, and exhibiting some instance use circumstances.
  • Stay demos for numerous fashions and duties, akin to
  • Tutorials for deciphering any mannequin of your alternative, and addding a brand new interpretation methodology.
  • Code for deciphering/attacking fashions and visualizing the ends in the demo (e.g., sentiment evaluation).
  • Quotation:

     @inproceedingsWallace2019AllenNLP,
      Writer = Eric Wallace and Jens Tuyls and Junlin Wang and Sanjay Subramanian
      and Matt Gardner and Sameer Singh,
      Booktitle = ,
      Yr = ,
      Title =  : A Framework for Explaining Predictions of  Fashions
            


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