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Chenle Li

2 accepted papers

2023

Learning to Predict Persona Information for Dialogue Personalization without Explicit Persona Description

ACL 2023findings

Personalizing dialogue agents is important for dialogue systems to generate more specific,consistent, and engaging responses. However, most current dialogue personalization approaches rely on explicit persona descriptions during inference, which severely restricts its application. In this paper, we…

Cited by 5SourcePDFScholar
2021

Learning from Perturbations: Diverse and Informative Dialogue Generation with Inverse Adversarial Training

ACL 2021long

In this paper, we propose Inverse Adversarial Training (IAT) algorithm for training neural dialogue systems to avoid generic responses and model dialogue history better. In contrast to standard adversarial training algorithms, IAT encourages the model to be sensitive to the perturbation in the dialo…

Cited by 25SourcePDFScholar