@inproceedings{song-etal-2021-deepblueai,
title = "{D}eep{B}lue{AI} at {WANLP}-{EACL}2021 task 2: A Deep Ensemble-based Method for Sarcasm and Sentiment Detection in {A}rabic",
author = "Song, Bingyan and
Pan, Chunguang and
Wang, Shengguang and
Luo, Zhipeng",
editor = "Habash, Nizar and
Bouamor, Houda and
Hajj, Hazem and
Magdy, Walid and
Zaghouani, Wajdi and
Bougares, Fethi and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Touileb, Samia",
booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
month = apr,
year = "2021",
address = "Kyiv, Ukraine (Virtual)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wanlp-1.52",
pages = "390--394",
abstract = "Sarcasm is one of the main challenges for sentiment analysis systems due to using implicit indirect phrasing for expressing opinions, especially in Arabic. This paper presents the system we submitted to the Sarcasm and Sentiment Detection task of WANLP-2021 that is capable of dealing with both two subtasks. We first perform fine-tuning on two kinds of pre-trained language models (PLMs) with different training strategies. Then an effective stacking mechanism is applied on top of the fine-tuned PLMs to obtain the final prediction. Experimental results on ArSarcasm-v2 dataset show the effectiveness of our method and we rank third and second for subtask 1 and 2.",
}
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<abstract>Sarcasm is one of the main challenges for sentiment analysis systems due to using implicit indirect phrasing for expressing opinions, especially in Arabic. This paper presents the system we submitted to the Sarcasm and Sentiment Detection task of WANLP-2021 that is capable of dealing with both two subtasks. We first perform fine-tuning on two kinds of pre-trained language models (PLMs) with different training strategies. Then an effective stacking mechanism is applied on top of the fine-tuned PLMs to obtain the final prediction. Experimental results on ArSarcasm-v2 dataset show the effectiveness of our method and we rank third and second for subtask 1 and 2.</abstract>
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%0 Conference Proceedings
%T DeepBlueAI at WANLP-EACL2021 task 2: A Deep Ensemble-based Method for Sarcasm and Sentiment Detection in Arabic
%A Song, Bingyan
%A Pan, Chunguang
%A Wang, Shengguang
%A Luo, Zhipeng
%Y Habash, Nizar
%Y Bouamor, Houda
%Y Hajj, Hazem
%Y Magdy, Walid
%Y Zaghouani, Wajdi
%Y Bougares, Fethi
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Touileb, Samia
%S Proceedings of the Sixth Arabic Natural Language Processing Workshop
%D 2021
%8 April
%I Association for Computational Linguistics
%C Kyiv, Ukraine (Virtual)
%F song-etal-2021-deepblueai
%X Sarcasm is one of the main challenges for sentiment analysis systems due to using implicit indirect phrasing for expressing opinions, especially in Arabic. This paper presents the system we submitted to the Sarcasm and Sentiment Detection task of WANLP-2021 that is capable of dealing with both two subtasks. We first perform fine-tuning on two kinds of pre-trained language models (PLMs) with different training strategies. Then an effective stacking mechanism is applied on top of the fine-tuned PLMs to obtain the final prediction. Experimental results on ArSarcasm-v2 dataset show the effectiveness of our method and we rank third and second for subtask 1 and 2.
%U https://aclanthology.org/2021.wanlp-1.52
%P 390-394
Markdown (Informal)
[DeepBlueAI at WANLP-EACL2021 task 2: A Deep Ensemble-based Method for Sarcasm and Sentiment Detection in Arabic](https://aclanthology.org/2021.wanlp-1.52) (Song et al., WANLP 2021)
ACL