Papers by Daijun Ding
EDDA: An Encoder-Decoder Data Augmentation Framework for Zero-Shot Stance Detection (2024.lrec-main)
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| Challenge: | Existing methods for enhancing text or data are limited by lack of logical connections between generated texts and training data. |
| Approach: | They propose an encoder-decoder data augmentation framework that combines large language models and chain-of-thought prompting to summarize texts into target-specific if-then rationales, establishing logical relationships. |
| Outcome: | The proposed framework significantly improves over state-of-the-art methods on benchmark datasets while enabling interpretable rationale-based learning. |