Abstract
Identifying whether a word carries the same meaning or different meaning in two contexts is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. Most of the previous work in this area rely on language-specific resources making it difficult to generalise across languages. Considering this limitation, our approach to SemEval-2021 Task 2 is based only on pretrained transformer models and does not use any language-specific processing and resources. Despite that, our best model achieves 0.90 accuracy for English-English subtask which is very compatible compared to the best result of the subtask; 0.93 accuracy. Our approach also achieves satisfactory results in other monolingual and cross-lingual language pairs as well.- Anthology ID:
- 2021.semeval-1.102
- Volume:
- Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 771–779
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.102
- DOI:
- 10.18653/v1/2021.semeval-1.102
- Cite (ACL):
- Hansi Hettiarachchi and Tharindu Ranasinghe. 2021. TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and Cross-lingual Word-in-Context Disambiguation. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 771–779, Online. Association for Computational Linguistics.
- Cite (Informal):
- TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and Cross-lingual Word-in-Context Disambiguation (Hettiarachchi & Ranasinghe, SemEval 2021)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2021.semeval-1.102.pdf
- Data
- WiC