2021-01-01 · conference-paper · Domain Adaptation and Few-Shot Learning · en
highly citedfrom the source record
Tianyu Gao, Adam Fisch, Danqi Chen. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.
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This paper addresses domain adaptation and few-shot learning and is a conference paper, so it has passed through formal peer review and should be weighted as a published conference contribution.
2 cited facts
This work was produced by a small team of 3 researchers.
1 cited fact
The work has received 1,359 citations, a figure that marks it as already an established reference point in its field. It references 53 works.
3 cited facts
Making Pre-trained Language Models Better Few-shot Learners is a conference-paper, focusing on Domain Adaptation and Few-Shot Learning, published in 2021.
Making Pre-trained Language Models Better Few-shot Learners has 1,359 citations in our record, placing it among the highly cited works we track.
Our record lists 3 authors for Making Pre-trained Language Models Better Few-shot Learners and references 53 other works. Author names aren't in our data yet — the source link on the page lists the full byline.
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Sources: openalex_works