Analysis Methods in Neural Language Processing: A Survey
Computation and Language
2019-01-15 v2 Machine Learning
Neural and Evolutionary Computing
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 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.
Cite
@article{arxiv.1812.08951,
title = {Analysis Methods in Neural Language Processing: A Survey},
author = {Yonatan Belinkov and James Glass},
journal= {arXiv preprint arXiv:1812.08951},
year = {2019}
}
Comments
Version including the supplementary materials (3 tables), also available at https://boknilev.github.io/nlp-analysis-methods