Analysis Methods in Neural Language Processing: A Survey Yonatan Belinkov1;2 and James Glass1 1MIT Computer Science and Artificial Intelligence Laboratory 2Harvard School of Engineering and Applied Sciences Cambridge, MA, USA fbelinkov,
[email protected] Abstract the networks in different ways.1 Others strive to better understand how NLP models work. This The field of natural language processing has theme of analyzing neural networks has connec- seen impressive progress in recent years, with tions to the broader work on interpretability in neural network models replacing many of the machine learning, along with specific characteris- traditional systems. A plethora of new mod- tics of the NLP field. els have been proposed, many of which are Why should we analyze our neural NLP mod- thought to be opaque compared to their feature- els? To some extent, this question falls into rich counterparts. This has led researchers to the larger question of interpretability in machine analyze, interpret, and evaluate neural net- learning, which has been the subject of much works in novel and more fine-grained ways. In debate in recent years.2 Arguments in favor this survey paper, we review analysis meth- of interpretability in machine learning usually ods in neural language processing, categorize mention goals like accountability, trust, fairness, them according to prominent research trends, safety, and reliability (Doshi-Velez and Kim, highlight existing limitations, and point to po- 2017; Lipton, 2016). Arguments against inter- tential directions for future work. pretability typically stress performance as the most important desideratum. All these arguments naturally apply to machine learning applications in NLP. 1 Introduction In the context of NLP, this question needs to be understood in light of earlier NLP work, often The rise of deep learning has transformed the field referred to as feature-rich or feature-engineered of natural language processing (NLP) in recent systems.