NCU-NLP at ROCLING-2021 Shared Task: Using MacBERT Transformers for Dimensional Sentiment Analysis

Man Chen Hung; Chao Yi Chen; Pin Jung Chen; Lung Hao Lee

NCU-NLP at ROCLING-2021 Shared Task: Using MacBERT Transformers for Dimensional Sentiment Analysis

Man Chen Hung, Chao Yi Chen, Pin Jung Chen, Lung Hao Lee

電機工程學系

研究成果: 書貢獻/報告類型 › 會議論文篇章 › 同行評審

3 引文斯高帕斯（Scopus）

摘要

We use the MacBERT transformers and fine-tune them to ROCLING-2021 shared tasks using the CVAT and CVAS data. We compare the performance of MacBERT with the other two transformers BERT and RoBERTa in the valence and arousal dimensions, respectively. MAE and correlation coefficient (r) were used as evaluation metrics. On ROCLING-2021 test set, our used MacBERT model achieves 0.611 of MAE and 0.904 of r in the valence dimensions; and 0.938 of MAE and 0.549 of r in the arousal dimension.

原文	???core.languages.en_GB???
主出版物標題	ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing
編輯	Lung-Hao Lee, Chia-Hui Chang, Kuan-Yu Chen
發行者	The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
頁面	380-384
頁數	5
ISBN（電子）	9789869576949
出版狀態	已出版 - 2021
事件	33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021 - Taoyuan, Taiwan 持續時間: 15 10月 2021 → 16 10月 2021

出版系列

名字	ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021
國家/地區	Taiwan
城市	Taoyuan
期間	15/10/21 → 16/10/21

中文醫療健康照護知識庫建置與應用(2/3)
Lee, L.-H. (PI)
1/05/20 → 30/04/21
研究計畫: Research

引用此

Hung, M. C., Chen, C. Y., Chen, P. J., & Lee, L. H. (2021). NCU-NLP at ROCLING-2021 Shared Task: Using MacBERT Transformers for Dimensional Sentiment Analysis. 於 L.-H. Lee, C.-H. Chang, & K.-Y. Chen (編輯), ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (頁 380-384). (ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing). The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).

Hung, Man Chen ; Chen, Chao Yi ; Chen, Pin Jung 等. / NCU-NLP at ROCLING-2021 Shared Task : Using MacBERT Transformers for Dimensional Sentiment Analysis. ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. 編輯 / Lung-Hao Lee ; Chia-Hui Chang ; Kuan-Yu Chen. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), 2021. 頁 380-384 (ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing).

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title = "NCU-NLP at ROCLING-2021 Shared Task: Using MacBERT Transformers for Dimensional Sentiment Analysis",

abstract = "We use the MacBERT transformers and fine-tune them to ROCLING-2021 shared tasks using the CVAT and CVAS data. We compare the performance of MacBERT with the other two transformers BERT and RoBERTa in the valence and arousal dimensions, respectively. MAE and correlation coefficient (r) were used as evaluation metrics. On ROCLING-2021 test set, our used MacBERT model achieves 0.611 of MAE and 0.904 of r in the valence dimensions; and 0.938 of MAE and 0.549 of r in the arousal dimension.",

keywords = "Affective computing, Deep learning, Learning emotions",

author = "Hung, {Man Chen} and Chen, {Chao Yi} and Chen, {Pin Jung} and Lee, {Lung Hao}",

note = "Publisher Copyright: {\textcopyright} 2021 ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. All rights reserved.; 33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021 ; Conference date: 15-10-2021 Through 16-10-2021",

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series = "ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing",

publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",

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booktitle = "ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing",

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Hung, MC, Chen, CY, Chen, PJ & Lee, LH 2021, NCU-NLP at ROCLING-2021 Shared Task: Using MacBERT Transformers for Dimensional Sentiment Analysis. 於 L-H Lee, C-H Chang & K-Y Chen (編輯), ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing, The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), 頁 380-384, 33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021, Taoyuan, Taiwan, 15/10/21.

NCU-NLP at ROCLING-2021 Shared Task: Using MacBERT Transformers for Dimensional Sentiment Analysis. / Hung, Man Chen; Chen, Chao Yi; Chen, Pin Jung 等.
ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. 編輯 / Lung-Hao Lee; Chia-Hui Chang; Kuan-Yu Chen. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), 2021. p. 380-384 (ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing).

研究成果: 書貢獻/報告類型 › 會議論文篇章 › 同行評審

TY - GEN

T1 - NCU-NLP at ROCLING-2021 Shared Task

T2 - 33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021

AU - Hung, Man Chen

AU - Chen, Chao Yi

AU - Chen, Pin Jung

AU - Lee, Lung Hao

PY - 2021

Y1 - 2021

N2 - We use the MacBERT transformers and fine-tune them to ROCLING-2021 shared tasks using the CVAT and CVAS data. We compare the performance of MacBERT with the other two transformers BERT and RoBERTa in the valence and arousal dimensions, respectively. MAE and correlation coefficient (r) were used as evaluation metrics. On ROCLING-2021 test set, our used MacBERT model achieves 0.611 of MAE and 0.904 of r in the valence dimensions; and 0.938 of MAE and 0.549 of r in the arousal dimension.

AB - We use the MacBERT transformers and fine-tune them to ROCLING-2021 shared tasks using the CVAT and CVAS data. We compare the performance of MacBERT with the other two transformers BERT and RoBERTa in the valence and arousal dimensions, respectively. MAE and correlation coefficient (r) were used as evaluation metrics. On ROCLING-2021 test set, our used MacBERT model achieves 0.611 of MAE and 0.904 of r in the valence dimensions; and 0.938 of MAE and 0.549 of r in the arousal dimension.

KW - Affective computing

KW - Deep learning

KW - Learning emotions

UR - http://www.scopus.com/inward/record.url?scp=85127436153&partnerID=8YFLogxK

M3 - 會議論文篇章

AN - SCOPUS:85127436153

T3 - ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing

SP - 380

EP - 384

BT - ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing

A2 - Lee, Lung-Hao

A2 - Chang, Chia-Hui

A2 - Chen, Kuan-Yu

PB - The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)

Y2 - 15 October 2021 through 16 October 2021

ER -

Hung MC, Chen CY, Chen PJ, Lee LH. NCU-NLP at ROCLING-2021 Shared Task: Using MacBERT Transformers for Dimensional Sentiment Analysis. 於 Lee LH, Chang CH, Chen KY, 編輯, ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP). 2021. p. 380-384. (ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing).

NCU-NLP at ROCLING-2021 Shared Task: Using MacBERT Transformers for Dimensional Sentiment Analysis

摘要

出版系列

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中文醫療健康照護知識庫建置與應用(2/3)

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