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Neural NLP models are increasingly accurate but are imperfect andopaque---they break in counterintuitive ways and leave end users puzzled attheir behavior. Model interpretation methods ameliorate this opacity byproviding explanations for specific model predictions. Unfortunately, existinginterpretation codebases make it difficult to apply these methods to new modelsand tasks, which hinders adoption for practitioners and burdensinterpretability researchers. We introduce AllenNLP Interpret, a flexibleframework for interpreting NLP models. The toolkit provides interpretationprimitives (e.g., input gradients) for any AllenNLP model and task, a suite ofbuilt-in interpretation methods, and a library of front-end visualizationcomponents. We demonstrate the toolkit's flexibility and utility byimplementing live demos for five interpretation methods (e.g., saliency mapsand adversarial attacks) on a variety of models and tasks (e.g., maskedlanguage modeling using BERT and reading comprehension using BiDAF). Thesedemos, alongside our code and tutorials, are available athttps://allennlp.org/interpret .

AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models