Efficient Methods for Natural Language Processing: A Survey
It provides guidance for conducting NLP under limited resources, which is an incremental contribution as it summarizes existing work without introducing new methods.
This survey addresses the problem of high resource consumption in natural language processing (NLP) due to scaling models and data, synthesizing current methods to achieve similar results with fewer resources.
Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows. Such resources include data, time, storage, or energy, all of which are naturally limited and unevenly distributed. This motivates research into efficient methods that require fewer resources to achieve similar results. This survey synthesizes and relates current methods and findings in efficient NLP. We aim to provide both guidance for conducting NLP under limited resources, and point towards promising research directions for developing more efficient methods.