Papers by Ronald Seoh
EmoGist: Efficient In-Context Learning for Visual Emotion Understanding (2025.findings-emnlp)
Copied to clipboard
| Challenge: | EmoGist is a training-free, in-context learning method for visual emotion classification . context-dependent definitions of emotion labels could allow more accurate predictions of emotions . |
| Approach: | They introduce EmoGist, a training-free, in-context learning method for performing visual emotion classification with LVLMs. |
| Outcome: | The proposed method improves micro F1 scores and macro F1 with LVLMs. |
Open Aspect Target Sentiment Classification with Natural Language Prompts (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing aspects target sentiment classification models are not trainable if annotated data are not available. |
| Approach: | They propose an approach that solves ATSC with natural language prompts by 24.13 accuracy points and 33.14 macro F1 points. |
| Outcome: | The proposed model outperforms supervised SOTA approaches under few-shot scenarios and under supervised settings, especially for few-shot cases. |