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This paper is a write-up for the tutorial on "Fine-grained Interpretation andCausation Analysis in Deep NLP Models" that we are presenting at NAACL 2021. Wepresent and discuss the research work on interpreting fine-grained componentsof a model from two perspectives, i) fine-grained interpretation, ii) causationanalysis. The former introduces methods to analyze individual neurons and agroup of neurons with respect to a language property or a task. The latterstudies the role of neurons and input features in explaining decisions made bythe model. We also discuss application of neuron analysis such as networkmanipulation and domain adaptation. Moreover, we present two toolkits namelyNeuroX and Captum, that support functionalities discussed in this tutorial.

Fine-grained Interpretation and Causation Analysis in Deep NLP Models