Analysis Methods in Neural Language Processing: A Survey
It provides a comprehensive overview for researchers to understand and improve interpretability in NLP, but it is incremental as it synthesizes existing work.
The paper surveys analysis methods for neural language processing, categorizing them by research trends and identifying limitations and future directions.
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 models 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 networks in novel and more fine-grained ways. In this survey paper, we review analysis methods in neural language processing, categorize them according to prominent research trends, highlight existing limitations, and point to potential directions for future work.