UoB_UK at SemEval 2021 Task 2: Zero-Shot and Few-Shot Learning for Multi-lingual and Cross-lingual Word Sense Disambiguation.

Wei Li, Harish Tayyar Madabushi, Mark Lee


Abstract
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.
Anthology ID:
2021.semeval-1.97
Volume:
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
738–742
Language:
URL:
https://aclanthology.org/2021.semeval-1.97
DOI:
10.18653/v1/2021.semeval-1.97
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.97.pdf