A Personalized Dialogue Generator with Implicit User Persona Detection (2022)

Abstract: Current works in the generation of personalized dialogue primarily contribute to the agent presenting a consistent personality and driving a more informative response. However, we found that the generated responses from most previous models tend to be self-centered, with little care for the user in the dialogue. Moreover, we consider that human-like conversation is essentially built based on inferring information about the persona of the other party. Motivated by this, we propose a novel personalized dialogue generator by detecting an implicit user persona. Because it is hard to collect a large number of detailed personas for each user, we attempted to model the user’s potential persona and its representation from dialogue history, with no external knowledge. The perception and fader variables were conceived using conditional variational inference. The two latent variables simulate the process of people being aware of each other’s persona and producing a corresponding expression in conversation. Finally, posterior-discriminated regularization was presented to enhance the training procedure. Empirical studies demonstrate that, compared to state-of-the-art methods, our approach is more concerned with the user’s persona and achieves a considerable boost across both automatic metrics and human evaluations.

## introduction personalized dialogue modeling is an attractive research topic in deep learning, where studies have explored the possibility of incorporating personal facts into the end-to-end generative framework. the established practice of assigning agents a predefined character improves the engagingness and consistency of open-domain dialogue. however, such models cannot generate distinguishable responses while interacting with different users because they do not take into consideration who the * equal contribution. other party is. as @xcite pointed out, a good chit-chat bot not only generates interesting responses but also resonates with interlocutors. however, there has been little research conducted into how to make the agent effectively mine a user's persona to generate customized responses. to this end, this research studied personalized dialogue generation in which we aimed to have the agent recognize the other party's potential persona by exploiting the dialogue itself and output personalized responses conditioned on the different target users. a simple illustration depicting this process is provided in figure 1 . inspired by the impressive effectiveness of conditional variational autoencoders (cvaes) @xcite with diverse response modeling, we propose a personalized dialogue generator that detects an implicit user persona using conditional variational inference. specifically, our model fits the profile descriptions of the other party to a multivariate isotropic gaussian distribution using a latent variable (perception variable) during training. because responses from the real-world dialogue are not always persona-related (i.e., persona-sparse issue; @xcite , we also introduce another latent variable (fader variable) to control the weight of persona-related aspects exhibited in the response. during inference, the decoder is designed to acquire the persona features from the perception and fader variables to produce a response that incorporates the user's various potential persona information inferred from the context. note that the textual profiles are only leveraged during training that is tasked with learning the latent distribution over the user's persona. and during inference, the raw observed data that yields latent variables only includes the context without the explicit persona. we argue that it is impractical to collect a large quantity of available profiles involved with specific users. thus, our model has better universality than methods that require providing extra information as generation material. cvaes have been proved to improve the response diversity at the discourse level (i.e., one-to-many nature; @xcite . our model achieves "one context to many responses" by sampling and reconstructing with stochasticity for persona distribution and responses, just as we can initiate different chats with a user from aspects of the user's persona. experimental results on the convai2 dataset demonstrate the superiority of the proposed model over the baselines in both automatic metrics and human evaluations. the interpretability and effectiveness of our approach are clarified in the discussion. the main contributions of this paper can be summarized as: ## conclusion and future work this paper presented a new implicit persona detection generator to achieve a user-personalized response. we establish the persona exploration and dialogue generation in a unified framework that supplies a way of leveraging the potential facts in dialogue. experiments on a large public dataset demonstrated that our approach had superior performance in producing user-specific responses. humans typically continue exchanges by drilling into the content of the conversation. from this perspective, the personaldialog dataset @xcite may be more appropriate for our approach. in the future, we plan to use this dataset to study if the inference of implicit personas can be strengthened. we would also conduct further experiments to examine whether there is an interpretable association between the prior network and the recognition network in terms of what they have learned. eventually, we plan to perfect l p o-di by having weights adaptively regulate the kl divergence.