Interpretability and Analysis in Neural NLP
Yonatan Belinkov, Sebastian Gehrmann, Ellie Pavlick
Abstract
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.
Topics & Concepts
InterpretabilityArtificial intelligenceComputer scienceField (mathematics)Artificial neural networkDeep learningNatural language processingFocus (optics)Interpretation (philosophy)Machine learningComputational linguisticsMathematicsPure mathematicsProgramming languagePhysicsOpticsTopic ModelingNatural Language Processing TechniquesExplainable Artificial Intelligence (XAI)