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Sogou Machine Reading Comprehension Toolkit
Machine reading comprehension have been intensively studied in recent ye...
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Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions
Modern deep learning models for NLP are notoriously opaque. This has mot...
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Interpretation of NLP models through input marginalization
To demystify the "black box" property of deep neural networks for natura...
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Self-Explaining Structures Improve NLP Models
Existing approaches to explaining deep learning models in NLP usually su...
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Gradient-based Analysis of NLP Models is Manipulable
Gradient-based analysis methods, such as saliency map visualizations and...
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An unexpected unity among methods for interpreting model predictions
Understanding why a model made a certain prediction is crucial in many d...
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Universal Adversarial Triggers for NLP
Adversarial examples highlight model vulnerabilities and are useful for ...
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AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models
Neural NLP models are increasingly accurate but are imperfect and opaque---they break in counterintuitive ways and leave end users puzzled at their behavior. Model interpretation methods ameliorate this opacity by providing explanations for specific model predictions. Unfortunately, existing interpretation codebases make it difficult to apply these methods to new models and tasks, which hinders adoption for practitioners and burdens interpretability researchers. We introduce AllenNLP Interpret, a flexible framework for interpreting NLP models. The toolkit provides interpretation primitives (e.g., input gradients) for any AllenNLP model and task, a suite of built-in interpretation methods, and a library of front-end visualization components. We demonstrate the toolkit's flexibility and utility by implementing live demos for five interpretation methods (e.g., saliency maps and adversarial attacks) on a variety of models and tasks (e.g., masked language modeling using BERT and reading comprehension using BiDAF). These demos, alongside our code and tutorials, are available at https://allennlp.org/interpret .
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