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arXiv · 2012.13190

QUACKIE: A NLP Classification Task With Ground Truth Explanations

Yves Rychener·Xavier Renard·Djamé Seddah·Pascal Frossard·Marcin Detyniecki
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Abstract

NLP Interpretability aims to increase trust in model predictions. This makes evaluating interpretability approaches a pressing issue. There are multiple datasets for evaluating NLP Interpretability, but their dependence on human provided ground truths raises questions about their unbiasedness. In this work, we take a different approach and formulate a specific classification task by diverting question-answering datasets. For this custom classification task, the interpretability ground-truth arises directly from the definition of the classification problem. We use this method to propose a benchmark and lay the groundwork for future research in NLP interpretability by evaluating a wide range of current state of the art methods.

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Yves Rychener, Xavier Renard, Djamé Seddah, Pascal Frossard, Marcin Detyniecki. 2020-12-24. QUACKIE: A NLP Classification Task With Ground Truth Explanations. https://arxiv.org/abs/2012.13190

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