Abstract
We present NEAMER - Named Entity Augmented Multi-word Expression Recognizer. This system is inspired by non-compositionality characteristics shared between Named Entity and Idiomatic Expressions. We utilize transfer learning and locality features to enhance idiom classification task. This system is our submission for SemEval Task 2: Multilingual Idiomaticity Detection and Sentence Embedding Subtask A OneShot shared task. We achieve SOTA with F1 0.9395 during post-evaluation phase. We also observe improvement in training stability. Lastly, we experiment with non-compositionality knowledge transfer, cross-lingual fine-tuning and locality features, which we also introduce in this paper.- Anthology ID:
- 2022.semeval-1.21
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 178–185
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.21
- DOI:
- 10.18653/v1/2022.semeval-1.21
- Cite (ACL):
- Min Sik Oh. 2022. kpfriends at SemEval-2022 Task 2: NEAMER - Named Entity Augmented Multi-word Expression Recognizer. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 178–185, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- kpfriends at SemEval-2022 Task 2: NEAMER - Named Entity Augmented Multi-word Expression Recognizer (Oh, SemEval 2022)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2022.semeval-1.21.pdf
- Data
- CoNLL-2003