Abstract
The same multi-word expressions may have different meanings in different sentences. They can be mainly divided into two categories, which are literal meaning and idiomatic meaning. Non-contextual-based methods perform poorly on this problem, and we need contextual embedding to understand the idiomatic meaning of multi-word expressions correctly. We use a pre-trained language model, which can provide a context-aware sentence embedding, to detect whether multi-word expression in the sentence is idiomatic usage.- Anthology ID:
- 2022.semeval-1.28
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 221–227
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.28
- DOI:
- 10.18653/v1/2022.semeval-1.28
- Cite (ACL):
- Zheng Chu, Ziqing Yang, Yiming Cui, Zhigang Chen, and Ming Liu. 2022. HIT at SemEval-2022 Task 2: Pre-trained Language Model for Idioms Detection. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 221–227, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- HIT at SemEval-2022 Task 2: Pre-trained Language Model for Idioms Detection (Chu et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/2022.semeval-1.28.pdf