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        <title>Generative Pretrained Transformers for Emotion Detection in a Code-Switching Setting</title>
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            <title>Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, &amp; Social Media Analysis</title>
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            <namePart type="given">Jeremy</namePart>
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    <abstract>This paper describes the approach that we utilized to participate in the shared task for multi-label and multi-class emotion classification organized as part of WASSA 2023 at ACL 2023. The objective was to build mod- els that can predict 11 classes of emotions, or the lack thereof (neutral class) based on code- mixed Roman Urdu and English SMS text messages. We participated in Track 2 of this task - multi-class emotion classification (MCEC). We used generative pretrained transformers, namely ChatGPT because it has a commercially available full-scale API, for the emotion detec- tion task by leveraging the prompt engineer- ing and zero-shot / few-shot learning method- ologies based on multiple experiments on the dev set. Although this was the first time we used a GPT model for the purpose, this ap- proach allowed us to beat our own baseline character-based XGBClassifier, as well as the baseline model trained by the organizers (bert- base-multilingual-cased). We ranked 4th and achieved the macro F1 score of 0.7038 and the accuracy of 0.7313 on the blind test set.</abstract>
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    <identifier type="doi">10.18653/v1/2023.wassa-1.61</identifier>
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%0 Conference Proceedings
%T Generative Pretrained Transformers for Emotion Detection in a Code-Switching Setting
%A Nedilko, Andrew
%Y Barnes, Jeremy
%Y De Clercq, Orphée
%Y Klinger, Roman
%S Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F nedilko-2023-generative
%X This paper describes the approach that we utilized to participate in the shared task for multi-label and multi-class emotion classification organized as part of WASSA 2023 at ACL 2023. The objective was to build mod- els that can predict 11 classes of emotions, or the lack thereof (neutral class) based on code- mixed Roman Urdu and English SMS text messages. We participated in Track 2 of this task - multi-class emotion classification (MCEC). We used generative pretrained transformers, namely ChatGPT because it has a commercially available full-scale API, for the emotion detec- tion task by leveraging the prompt engineer- ing and zero-shot / few-shot learning method- ologies based on multiple experiments on the dev set. Although this was the first time we used a GPT model for the purpose, this ap- proach allowed us to beat our own baseline character-based XGBClassifier, as well as the baseline model trained by the organizers (bert- base-multilingual-cased). We ranked 4th and achieved the macro F1 score of 0.7038 and the accuracy of 0.7313 on the blind test set.
%R 10.18653/v1/2023.wassa-1.61
%U https://aclanthology.org/2023.wassa-1.61
%U https://doi.org/10.18653/v1/2023.wassa-1.61
%P 616-620

Markdown (Informal)

[Generative Pretrained Transformers for Emotion Detection in a Code-Switching Setting](https://aclanthology.org/2023.wassa-1.61) (Nedilko, WASSA 2023)

• Generative Pretrained Transformers for Emotion Detection in a Code-Switching Setting (Nedilko, WASSA 2023)

ACL

• Andrew Nedilko. 2023. Generative Pretrained Transformers for Emotion Detection in a Code-Switching Setting. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 616–620, Toronto, Canada. Association for Computational Linguistics.