Papers by Jens Tuyls

2 papers
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models (D19-3)

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Challenge: Existing interpretation codebases make it difficult to apply these methods to new models and tasks.
Approach: They propose a framework for interpreting NLP models that provides explanations for specific models.
Outcome: The proposed framework provides interpretation primitives for any AllenNLP model and task, a suite of built-in interpretation methods, and a library of front-end visualization components.
Gradient-based Analysis of NLP Models is Manipulable (2020.findings-emnlp)

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Challenge: Recent work has shown that explanation techniques can be unstable and can be manipulated to hide the actual reasoning behind the predictions of NLP models.
Approach: They propose to merge a BERT-based sentiment classifier with a Facade Model that overwhelms the gradients without affecting the predictions.
Outcome: The proposed model overwhelms the gradients without affecting the predictions on a variety of NLP tasks, such as sentiment analysis, NLI, and QA.

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