Explainable AI

Current research on explainability and interpretability of machine learning algorithms

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Evaluating neural network explanation methods using hybrid documents and morphological agreement

Nina Poerner, Benjamin Roth, Hinrich Schütze. 2018

[ArXiv]    

The behavior of deep neural networks (DNNs) is hard to understand. This makes it necessary to explore post hoc explanation methods. We conduct the first comprehensive evaluation of explanation methods for NLP. To this end, we design two novel evaluation paradigms that cover two important classes of NLP problems: small context and large context problems. Both paradigms require no manual annotation and are therefore broadly applicable. We also introduce LIMSSE, an explanation method inspired by LIME that is designed for NLP. We show empirically that LIMSSE, LRP and DeepLIFT are the most effective explanation methods and recommend them for explaining DNNs in NLP.