Few-shot learning through contextual data augmentation

Autor: Rachel Bawden, Alexandra Birch, Farid Arthaud

Publikováno v: Artaud, F, Bawden, R & Birch, A 2021, Few-shot learning through contextual data augmentation . in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume . pp. 1049-1062, 16th conference of the European Chapter of the Association for Computational Linguistics, Virtual Conference, 19/04/21 . < https://www.aclweb.org/anthology/2021.eacl-main.90 >
EACL 2021-16th Conference of the European Chapter of the Association for Computational Linguistics
EACL 2021-16th Conference of the European Chapter of the Association for Computational Linguistics, Apr 2021, Kiev / Virtual, Ukraine
EACL
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Machine translation (MT) models used in industries with constantly changing topics, such as translation or news agencies, need to adapt to new data to maintain their performance over time. Our aim is to teach a pre-trained MT model to translate previ

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::94ad60a0b4a98c5716a2a374bad42395
https://www.pure.ed.ac.uk/ws/files/208727159/Few_shot_learning_ARTAUD_DOA01022021_VOR_CC_BY.pdf