Treviso, Marcos ; Lee, Ji-Ung ; Ji, Tianchu ; Aken, Betty van ; Cao, Qingqing ; Ciosici, Manuel R. ; Hassid, Michael ; Heafield, Kenneth ; Hooker, Sara ; Raffel, Colin ; Martins, Pedro H. ; Martins, André F. T. ; Forde, Jessica Zosa ; Milder, Peter Milder ; Simpson, Edwin ; Slonim, Noam ; Dodge, Jesse ; Strubell, Emma ; Balasubramaniam, Niranjan ; Derczynski, Leon ; Gurevych, Iryna ; Schwartz, Roy (2023)
Efficient Methods for Natural Language Processing: A Survey.
In: Transactions of the Association for Computational Linguistics, 11
doi: 10.1162/tacl_a_00577
Article, Bibliographie
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.
| Item Type: | Article |
|---|---|
| Erschienen: | 2023 |
| Creators: | Treviso, Marcos ; Lee, Ji-Ung ; Ji, Tianchu ; Aken, Betty van ; Cao, Qingqing ; Ciosici, Manuel R. ; Hassid, Michael ; Heafield, Kenneth ; Hooker, Sara ; Raffel, Colin ; Martins, Pedro H. ; Martins, André F. T. ; Forde, Jessica Zosa ; Milder, Peter Milder ; Simpson, Edwin ; Slonim, Noam ; Dodge, Jesse ; Strubell, Emma ; Balasubramaniam, Niranjan ; Derczynski, Leon ; Gurevych, Iryna ; Schwartz, Roy |
| Type of entry: | Bibliographie |
| Title: | Efficient Methods for Natural Language Processing: A Survey |
| Language: | English |
| Date: | 12 July 2023 |
| Publisher: | MIT Press |
| Journal or Publication Title: | Transactions of the Association for Computational Linguistics |
| Volume of the journal: | 11 |
| DOI: | 10.1162/tacl_a_00577 |
| 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. |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Ubiquitous Knowledge Processing |
| Date Deposited: | 25 Jul 2023 11:49 |
| Last Modified: | 26 Jul 2023 10:26 |
| PPN: | 509928870 |
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| Suche nach Titel in: | TUfind oder in Google |
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