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
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- 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
- Cite (ACL):
- Wei Li, Harish Tayyar Madabushi, and Mark Lee. 2021. UoB_UK at SemEval 2021 Task 2: Zero-Shot and Few-Shot Learning for Multi-lingual and Cross-lingual Word Sense Disambiguation.. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 738–742, Online. Association for Computational Linguistics.
- Cite (Informal):
- UoB_UK at SemEval 2021 Task 2: Zero-Shot and Few-Shot Learning for Multi-lingual and Cross-lingual Word Sense Disambiguation. (Li et al., SemEval 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.semeval-1.97.pdf