Interpretability and Analysis in Neural NLP
Yonatan Belinkov, Sebastian Gehrmann, Ellie Pavlick · 2020
While deep learning has transformed the natural language processing (NLP) field and impacted the larger computational linguistics community, the rise of neural networks is stained by their opaque nature: It is challenging to interpret the inner workings of neural network models, and explicate their behavior.Therefore, in the last few years, an increasingly large body of work has been devoted to the analysis and interpretation of neural network models in NLP.This body of work is so far lacking a common framework and methodology.Moreover, approaching the analysis of modern neural networks can be difficult for newcomers to the field.This tutorial aims to fill this gap and introduce the nascent field of interpretability and analysis of neural networks in NLP.