Abstract
This paper presents the team BRUMS submission to SemEval-2020 Task 3: Graded Word Similarity in Context. The system utilises state-of-the-art contextualised word embeddings, which have some task-specific adaptations, including stacked embeddings and average embeddings. Overall, the approach achieves good evaluation scores across all the languages, while maintaining simplicity. Following the final rankings, our approach is ranked within the top 5 solutions of each language while preserving the 1st position of Finnish subtask 2.- Anthology ID:
- 2020.semeval-1.16
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Editors:
- Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 142–149
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.16
- DOI:
- 10.18653/v1/2020.semeval-1.16
- Cite (ACL):
- Hansi Hettiarachchi and Tharindu Ranasinghe. 2020. BRUMS at SemEval-2020 Task 3: Contextualised Embeddings for Predicting the (Graded) Effect of Context in Word Similarity. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 142–149, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- BRUMS at SemEval-2020 Task 3: Contextualised Embeddings for Predicting the (Graded) Effect of Context in Word Similarity (Hettiarachchi & Ranasinghe, SemEval 2020)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2020.semeval-1.16.pdf
- Code
- HHansi/Semeval-2020-Task3