Papers by Min-Hsuan Yeh
Multi-VQG: Generating Engaging Questions for Multiple Images (2022.emnlp-main)
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| Challenge: | Traditional visual question generation (VQG) focuses on single images, resulting in a limited ability to comprehend time-series information of the underlying event. |
| Approach: | They propose to generate engaging questions from multiple images using a visual question generation dataset and establish a series of baselines. |
| Outcome: | The proposed model builds stories behind the image sequence to allow for creativity and experience sharing and hence draw attention to downstream applications. |
Lying Through One’s Teeth: A Study on Verbal Leakage Cues (2021.emnlp-main)
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| Challenge: | Existing studies on verbal leakage cues do not address their impact on models' validity. |
| Approach: | They propose to use LIWC to show verbal leakage cues in lie detection datasets to understand their effect on data collection and examine their validity. |
| Outcome: | The proposed models with more strong verbal leakage cue categories perform better than models trained on a dataset with only a greater number of strong cues. |
CoCoLoFa: A Dataset of News Comments with Common Logical Fallacies Written by LLM-Assisted Crowds (2024.emnlp-main)
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| Challenge: | Existing algorithms for detecting logical fallacies in texts are expensive and require large-scale labeled datasets. |
| Approach: | They introduce CoCoLoFa, the largest known logical fallacy dataset, with 7,706 comments for 648 news articles labeled for fallacy presence and type. |
| Outcome: | The proposed dataset outperforms state-of-the-art LLMs in fallacy detection and classification. |