@inproceedings{phan-etal-2022-nlp,
title = "{NLP}@{UIT} at {F}ig{L}ang-{EMNLP} 2022: A Divide-and-Conquer System For Shared Task On Understanding Figurative Language",
author = "Phan, Khoa Thi-Kim and
Nguyen, Duc-Vu and
Nguyen, Ngan Luu-Thuy",
editor = "Ghosh, Debanjan and
Beigman Klebanov, Beata and
Muresan, Smaranda and
Feldman, Anna and
Poria, Soujanya and
Chakrabarty, Tuhin",
booktitle = "Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.flp-1.21",
doi = "10.18653/v1/2022.flp-1.21",
pages = "150--153",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T NLP@UIT at FigLang-EMNLP 2022: A Divide-and-Conquer System For Shared Task On Understanding Figurative Language
%A Phan, Khoa Thi-Kim
%A Nguyen, Duc-Vu
%A Nguyen, Ngan Luu-Thuy
%Y Ghosh, Debanjan
%Y Beigman Klebanov, Beata
%Y Muresan, Smaranda
%Y Feldman, Anna
%Y Poria, Soujanya
%Y Chakrabarty, Tuhin
%S Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F phan-etal-2022-nlp
%X 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.
%R 10.18653/v1/2022.flp-1.21
%U https://aclanthology.org/2022.flp-1.21
%U https://doi.org/10.18653/v1/2022.flp-1.21
%P 150-153
Markdown (Informal)
[NLP@UIT at FigLang-EMNLP 2022: A Divide-and-Conquer System For Shared Task On Understanding Figurative Language](https://aclanthology.org/2022.flp-1.21) (Phan et al., Fig-Lang 2022)
ACL