Analysis Methods in Neural Language Processing: A Survey

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 {belinkov, glass}@mit.edu Abstract The field of natural language processing has seen impressive progress in recent years, with neural network models replacing many of the traditional systems. A plethora of new mod- els have been proposed, many of which are thought to be opaque compared to their feature- rich counterparts. This has led researchers to analyze, interpret, and evaluate neural net- works in novel and more fine-grained ways. In this survey paper, we review analysis meth- ods in neural language processing, categorize them according to prominent research trends, highlight existing limitations, and point to po- tential directions for future work. 1 Introduction The rise of deep learning has transformed the field of natural language processing (NLP) in recent years. Models based on neural networks have obtained impressive improvements in various tasks, including language modeling (Mikolov et al., 2010; Jozefowicz et al., 2016), syntactic parsing (Kiperwasser and Goldberg, 2016), machine translation (MT) (Bahdanau et al., 2014; Sutskever et al., 2014), and many other tasks; see Goldberg (2017) for example success stories. This progress has been accompanied by a myriad of new neural network architectures. In many cases, traditional feature-rich systems are being replaced by end-to-end neural networks that aim to map input text to some output pre- diction. As end-to-end systems are gaining preva- lence, one may point to two trends. First, some push back against the abandonment of linguis- tic knowledge and call for incorporating it inside the networks in different ways.1 Others strive to better understand how NLP models work. This theme of analyzing neural networks has connec- tions to the broader work on interpretability in machine learning, along with specific characteris- tics of the NLP field. Why should we analyze our neural NLP mod- els? To some extent, this question falls into the larger question of interpretability in machine learning, which has been the subject of much debate in recent years.2 Arguments in favor of interpretability in machine learning usually mention goals like accountability, trust, fairness, safety, and reliability (Doshi-Velez and Kim, 2017; Lipton, 2016). Arguments against inter- pretability typically stress performance as the most important desideratum. All these arguments naturally apply to machine learning applications in NLP. In the context of NLP, this question needs to be understood in light of earlier NLP work, often referred to as feature-rich or feature-engineered systems. In some of these systems, features are more easily understood by humans—they can be morphological properties, lexical classes, syn- tactic categories, semantic relations, etc. In theory, one could observe the importance assigned by statistical NLP models to such features in order to gain a better understanding of the model.3 In 1See, for instance, Noah Smith’s invited talk at ACL 2017: vimeo.com/234958746. See also a recent debate on this matter by Chris Manning and Yann LeCun: www. youtube.com/watch?v=fKk9KhGRBdI.(Videosaccessed on December 11, 2018.) 2See, for example, the NIPS …

Content truncated to 500 words