Optimizing training data for persona-grounded dialogue via Synthetic Label Augmentation

Persona-grounded dialogue systems aim to enhance the quality of AI agent responses by bolstering persona consistency and promoting response diversity. Although model tuning has seen significant advancements, there is an ongoing need to refine the training data itself. Expanding the scope of personas has been suggested as a means to bridge this gap. Nevertheless, the lack of gold labels that align with these expanded personas poses a challenge for AI agents in training the extent of real-world knowledge. To tackle these challenges, we propose the Synthetic Label Augmentation framework. This framework (1) creates a background skeleton from the original gold labels, masking persona-related elements, (2) infuses the background skeleton with expanded-persona features, generating synthetic gold labels, (3) identifies the most appropriate synthetic gold labels among the candidates, and (4) merges them into persona-grounded dialogue dataset. Through extensive experiments on the Persona-Chat, we demonstrate that the proposed framework effectively integrates the content of expanded personas to generate synthetic gold labels suitable for the dialogue context. Furthermore, response generation experiments using the Optimized Persona-Chat show that our framework significantly enhances AI agents' performance in terms of persona consistency and response diversity.