Enhancing Personalized Dialogue Generation with Contrastive Latent Variables: Combining Sparse and Dense Persona (2023)

Abstract: The personalized dialogue explores the consistent relationship between dialogue generation and personality. Existing personalized dialogue agents model persona profiles from three resources: sparse or dense persona descriptions and dialogue histories. However, sparse structured persona attributes are explicit but uninformative, dense persona texts contain rich persona descriptions with much noise, and dialogue history query is both noisy and uninformative for persona modeling. In this work, we combine the advantages of the three resources to obtain a richer and more accurate persona. We design a Contrastive Latent Variable-based model (CLV) that clusters the dense persona descriptions into sparse categories, which are combined with the history query to generate personalized responses. Experimental results on Chinese and English datasets demonstrate our model’s superiority in personalization.

## introduction in order to develop personalized dialogue agents, current approaches enhance the personality of generated responses mainly utilizing three kinds of resources: (1) defined sparse persona attributes @xcite @xcite @xcite ; (2) dense persona description texts @xcite @xcite ; (3) historical queries of current dialogue @xcite . each of the three resources has its advantages and disadvantages. sparse persona attributes (e.g., gender, age) are highly interpretable and have high information utilization, but the information is limited and cannot express complex persona features. dense persona description text contains rich and flexible persona information but suffers from noisy expressions. modeling personality directly from dialogue histories is free of additional persona information, but the persona information in history queries is both noisy and uninformative. to address these issues, in this paper, we improve personalized dialogue generation by combining the advantages of the three resources. we design a contrastive latent variable (clv)-based model that clusters the dense persona descriptions into sparse categories, which are combined with the history query to generate personalized responses. specifically, first, the dialog's latest query and response together with dense persona description texts are encoded. then the recognition distribution of query and response is jointly modeled with a pre-designed dual conditional variational autoencoder (cvae @xcite ). simultaneously, the persona information is automatically separated into multiple parts to participate in the above process in parallel. these partitioned persona pieces of information are considered to hide different angles of portrayal. this process is also reinforced by contrastive learning. next, a decider decides which category of persona information is used for persona modeling. finally, a personalized generator combines the history query and additional persona information for response generation. without explicit supervised signals, we design a pseudo-labeling and joint training method to train the decider. our contributions are summarized as follows: ## further analysis we further describe our model through a series of analyses. all analyses are based on the convai2 dataset, and similar phenomena can be observed on baidu personachat. ## conclusion in this work, we propose a clv model for personalized dialogue generation. unlike existing works, we integrate the advantages of sparse and dense persona information. we use a self-separation mechanism to implicitly cluster the persona information in the dense persona information text so that the decider can consider different sparse categories of persona information during dialogue and enhance the personalization of dialogue generation. we also propose a more effective evaluation metric framework for personalized dialogue generation. the experimental results confirm the effectiveness of the model in generating personalized responses.