%0 Conference Proceedings
%T UoB_UK at SemEval 2021 Task 2: Zero-Shot and Few-Shot Learning for Multi-lingual and Cross-lingual Word Sense Disambiguation.
%A Li, Wei
%A Tayyar Madabushi, Harish
%A Lee, Mark
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Schluter, Natalie
%Y Emerson, Guy
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F li-etal-2021-uob
%X This paper describes our submission to SemEval 2021 Task 2. We compare XLM-RoBERTa Base and Large in the few-shot and zero-shot settings and additionally test the effectiveness of using a k-nearest neighbors classifier in the few-shot setting instead of the more traditional multi-layered perceptron. Our experiments on both the multi-lingual and cross-lingual data show that XLM-RoBERTa Large, unlike the Base version, seems to be able to more effectively transfer learning in a few-shot setting and that the k-nearest neighbors classifier is indeed a more powerful classifier than a multi-layered perceptron when used in few-shot learning.
%R 10.18653/v1/2021.semeval-1.97
%U https://aclanthology.org/2021.semeval-1.97
%U https://doi.org/10.18653/v1/2021.semeval-1.97
%P 738-742