“You are an expert annotator”: Automatic Best–Worst-Scaling Annotations for Emotion Intensity Modeling
Christopher Bagdon, Prathamesh Karmalkar, Harsha Gurulingappa, Roman Klinger
Abstract
Labeling corpora constitutes a bottleneck to create models for new tasks or domains. Large language models mitigate the issue with automatic corpus labeling methods, particularly for categorical annotations. Some NLP tasks such as emotion intensity prediction, however, require text regression, but there is no work on automating annotations for continuous label assignments. Regression is considered more challenging than classification: The fact that humans perform worse when tasked to choose values from a rating scale lead to comparative annotation methods, including best–worst scaling. This raises the question if large language model-based annotation methods show similar patterns, namely that they perform worse on rating scale annotation tasks than on comparative annotation tasks. To study this, we automate emotion intensity predictions and compare direct rating scale predictions, pairwise comparisons and best–worst scaling. We find that the latter shows the highest reliability. A transformer regressor fine-tuned on these data performs nearly on par with a model trained on the original manual annotations.- Anthology ID:
- 2024.naacl-long.439
- Volume:
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7924–7936
- Language:
- URL:
- https://aclanthology.org/2024.naacl-long.439
- DOI:
- 10.18653/v1/2024.naacl-long.439
- Cite (ACL):
- Christopher Bagdon, Prathamesh Karmalkar, Harsha Gurulingappa, and Roman Klinger. 2024. “You are an expert annotator”: Automatic Best–Worst-Scaling Annotations for Emotion Intensity Modeling. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 7924–7936, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- “You are an expert annotator”: Automatic Best–Worst-Scaling Annotations for Emotion Intensity Modeling (Bagdon et al., NAACL 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.naacl-long.439.pdf