Miracle: Towards Personalized Dialogue Generation with Latent-Space Multiple Personal Attribute Control (2023)

Abstract: Personalized dialogue systems aim to endow the chatbot agent with more anthropomorphic traits for human-like interactions. Previous approaches have explored explicitly user profile modeling using text descriptions, implicit derivation of user embeddings, or utilizing handicraft prompts for ChatGPT-like models. However, textual personas are limited in describing multi-faceted attributes ( e.g. , language style, inner character nuances ), implicit embedding suffers from personality sparsity, and handicraft prompts lack fine-grained and stable controllability. Hence, these approaches may struggle with complex personalized dialogue generation tasks that require generating controllable responses with multiple personal attributes. To this end, we propose Miracle , a novel personalized dialogue generation method through M ult I ple Pe R sonal A ttributes C ontrol within L atent-Space E nergy-based Models. ttributes C ontrol within L atent-Space E nergy-based Models. Specifically, our approach first disentangles complex personality into multi-faceted attributes. Subsequently, we employ a conditional variational auto-encoder to align with the dense personalized responses within a latent joint attribute space. We have also tailored a dedicated energy function and customized the ordinary differential equations sampling method to offer flexible attribute composition and precise attribute control. Extensive experiments demonstrate that Miracle outperforms several strong baselines in terms of personality controllability and response generation quality. Our dataset and code are available at https://github.com/LZY-the-boys/MIRACLE

## introduction building a personalized and anthropomorphic chatbot is an essential goal in the field of dialogue sys- * corresponding author. ## experiments to verify the effectiveness of our proposed mir-acle, we conduct extensive experiments on both automatic and human evaluations. additionally, we provide further analysis on ablation, efficiency, and case studies. ## conclusion in this paper, we propose miracle, a novel approach for personalized dialogue generation. our method leverages a composition of multiple personal attributes to model personality and formulates the generation of personalized responses as sampling from a specific energy-based model. we introduce a dialogue cvae aligning the joint attribute space with dialogue responses by employing two designed loss functions. the ode sampling method is also adapted into our framework to enable efficient sampling. experimental results demonstrate that our approach achieves state-ofthe-art performance by striking a fine balance between the quality of generated responses and the ability to control their personalization. furthermore, we curate a dataset of high-quality, singleaspect dialogue corpus, which serves as a valuable resource for further exploration and advancement personality lyrical + optimistic + critical