Abstract
This paper describes our submissions to the EMNLP 2022 shared task on Understanding Figurative Language as part of the Figurative Language Workshop (FigLang 2022). Our systems based on pre-trained language model T5 are divide-and-conquer models which can address both two requirements of the task: 1) classification, and 2) generation. In this paper, we introduce different approaches in which each approach we employ a processing strategy on input model. We also emphasize the influence of the types of figurative language on our systems.- Anthology ID:
- 2022.flp-1.21
- Volume:
- Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Debanjan Ghosh, Beata Beigman Klebanov, Smaranda Muresan, Anna Feldman, Soujanya Poria, Tuhin Chakrabarty
- Venue:
- Fig-Lang
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 150–153
- Language:
- URL:
- https://aclanthology.org/2022.flp-1.21
- DOI:
- 10.18653/v1/2022.flp-1.21
- Cite (ACL):
- Khoa Thi-Kim Phan, Duc-Vu Nguyen, and Ngan Luu-Thuy Nguyen. 2022. NLP@UIT at FigLang-EMNLP 2022: A Divide-and-Conquer System For Shared Task On Understanding Figurative Language. In Proceedings of the 3rd Workshop on Figurative Language Processing (FLP), pages 150–153, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- NLP@UIT at FigLang-EMNLP 2022: A Divide-and-Conquer System For Shared Task On Understanding Figurative Language (Phan et al., Fig-Lang 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2022.flp-1.21.pdf