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    <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.</abstract>
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%0 Conference Proceedings
%T TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and Cross-lingual Word-in-Context Disambiguation
%A Hettiarachchi, Hansi
%A Ranasinghe, Tharindu
%S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
%D 2021
%8 aug
%I Association for Computational Linguistics
%C Online
%F hettiarachchi-ranasinghe-2021-transwic
%X 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.
%R 10.18653/v1/2021.semeval-1.102
%U https://aclanthology.org/2021.semeval-1.102
%U https://doi.org/10.18653/v1/2021.semeval-1.102
%P 771-779

Markdown (Informal)

[TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and Cross-lingual Word-in-Context Disambiguation](https://aclanthology.org/2021.semeval-1.102) (Hettiarachchi & Ranasinghe, SemEval 2021)

• TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and Cross-lingual Word-in-Context Disambiguation (Hettiarachchi & Ranasinghe, SemEval 2021)

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.