Papers by Chenle Li
Learning to Predict Persona Information for Dialogue Personalization without Explicit Persona Description (2023.findings-acl)
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| Challenge: | Existing approaches to personalize dialogue agents rely on explicit persona descriptions during inference, which severely limits their application in real-world scenarios. |
| Approach: | They propose a method that learns to predict persona information based on the dialogue history to personalize dialogue agents without relying on explicit persona descriptions during inference. |
| Outcome: | The proposed method improves the consistency and engagingness of generated responses when conditioning on the predicted profile of the dialogue agent. |
Learning from Perturbations: Diverse and Informative Dialogue Generation with Inverse Adversarial Training (2021.acl-long)
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| Challenge: | Inverse Adversarial Training (IAT) algorithm for training neural dialogue systems to avoid generic responses and model dialogue history better. |
| Approach: | They propose an algorithm that encourages the model to be sensitive to perturbations in dialogue history and learn from perturbations. |
| Outcome: | The proposed approach can model dialogue history better and generate more diverse responses on two benchmark datasets. |