Efficient Methods for Natural Language Processing: A Survey
Abstrak
Abstract 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.
Topik & Kata Kunci
Penulis (19)
Marcos Vinícius Treviso
Tianchu Ji
Ji-Ung Lee
Betty van Aken
Qingqing Cao
Manuel R. Ciosici
Michael Hassid
Kenneth Heafield
Sara Hooker
Pedro Henrique Martins
André F. T. Martins
Jessica Zosa Forde
Peter Milder
Colin Raffel
Edwin Simpson
N. Slonim
Niranjan Balasubramanian
Leon Derczynski
Roy Schwartz
Akses Cepat
- Tahun Terbit
- 2022
- Bahasa
- en
- Total Sitasi
- 149×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1162/tacl_a_00577
- Akses
- Open Access ✓