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.- Anthology ID:
- 2023.wassa-1.61
- Volume:
- Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Jeremy Barnes, Orphée De Clercq, Roman Klinger
- Venue:
- WASSA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 616–620
- Language:
- URL:
- https://aclanthology.org/2023.wassa-1.61
- DOI:
- 10.18653/v1/2023.wassa-1.61
- Cite (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.
- Cite (Informal):
- Generative Pretrained Transformers for Emotion Detection in a Code-Switching Setting (Nedilko, WASSA 2023)
- PDF:
- https://preview.aclanthology.org/insights-reingestion/2023.wassa-1.61.pdf