Guiding Variational Response Generator to Exploit Persona (2020)

Abstract: Leveraging persona information of users in Neural Response Generators (NRG) to perform personalized conversations has been considered as an attractive and important topic in the research of conversational agents over the past few years. Despite of the promising progress achieved by recent studies in this field, persona information tends to be incorporated into neural networks in the form of user embeddings, with the expectation that the persona can be involved via End-to-End learning. This paper proposes to adopt the personality-related characteristics of human conversations into variational response generators, by designing a specific conditional variational autoencoder based deep model with two new regularization terms employed to the loss function, so as to guide the optimization towards the direction of generating both persona-aware and relevant responses. Besides, to reasonably evaluate the performances of various persona modeling approaches, this paper further presents three direct persona-oriented metrics from different perspectives. The experimental results have shown that our proposed methodology can notably improve the performance of persona-aware response generation, and the metrics are reasonable to evaluate the results.

## introduction as an essential research topic in generative conversational agents (a.k.a., chat-bots), persona modeling is of great importance for such deep neural network based intelligent interactive systems @xcite @xcite . apparently, user-personalitydependent responses provided by a chat-bot are able to significantly improve the consistency of its conversations, meanwhile, it is possible for users * * contribution during the internship at tencent. to flexibly customize the persona of a chat-bot based on some existent dialogues. as for the studies on this topic, with no doubt, incorporating persona factors into end-to-end generative models is an attractive topic with great challenges. the current studies mainly focus on adopting the explicit meta-data of user profiles @xcite or character @xcite @xcite to generate persona-aware responses. however, on one hand, user profiles are usually highly privacy-related and thus it is difficult to obtain such information from users practically. on the other hand, little correlation can be explicitly observed between such meta-data profiles and persona characteristics of users. especially, those character descriptions, tailor-made for the persona-aware response generation with the great cost of manual work, are only a variant of user profile innately in terms of different natural language forms. one of the reasonable and practically executable methodologies for introducing persona factors into conversation models is to adopt the real-valued user representation as a medium @xcite @xcite . in particular, such user representations can be derived from users' historical dialog utterances with rich linguistic and personality information involved. taking persona representations as the guidance for generating customized responses becomes a widely accepted methodology due to the recent development of deep latent variable models @xcite @xcite . however, for current models, without the explicit learning objectives or constraints, the user representation is adopted in a passive way to reduce the model loss and kl divergence via end-to-end learning. in this case, it is highly possible that the employed embeddings will not work as effectively as expected. consequently, it is necessary to employ explicit guidance to help variational response generators sense persona. from observations upon personacontained dialogs, there exist intuitive characteristics for directing the optimization of the personaaware variational response generation. obviously, for a given user, the appropriately modeled and leveraged persona information can help to generate hidden variables semantically relevant with corresponding responses. besides, since users may have their own linguistic style, the adoption of personal information in nrg aims to have direct influence on the degree of linguistic (e.g. lexical and syntactic) convergence for a specific user. this paper aims at exploring the explicit guidance to help the variational response generator exploit persona information hidden in the nonstructured contents produced by the users, by utilizing intuitive characteristics of personalized conversations for model training. the contributions of this paper can be summarized as follows: ## approach based on the current progress on the development of latent variable models, we propose a personaaware variational response generator to automatically exploit persona from conversations, and utilize such personal information to model the future conversation. besides, given that personal information can be exploited as optimization guidance to better modeling persona, we further introduce two regularization terms to guide the model learning. in the following section, we first describe the general structure of pagenerator, and then explain the two additional regularization terms. ## conclusions in this paper, we proposed a variational neural network to model the conversation as well as the persona of users. on the basis of the network, two regularization terms are designed to guide the model in emphasizing the importance of the hidden user information. in addition, to better reflect the persona characteristics of the response generation model, three metrics have been introduced to quantify the level of persona of the generated responses. experimental results show that our approach significantly outperforms other baseline models and the proposed metrics are effective in evaluating the capabilities of models on generating persona-aware responses. joern wuebker, patrick simianer, and john denero. 2018. compact personalized models for neural machine translation. in proceedings of the 2018 conference on empirical methods in natural language processing, pages 881-886. chen xing, wei wu, yu wu, jie liu, yalou huang, ming zhou, and wei-ying ma. 2017. topic aware neural response generation. in thirty-first aaai conference on artificial intelligence. saizheng zhang, emily dinan, jack urbanek, arthur szlam, douwe kiela, and jason weston. 2018. personalizing dialogue agents: i have a dog, do you have pets too? in proceedings of the 56th annual meeting of the association for computational linguistics (volume 1: long papers), pages 2204-2213. association for computational linguistics. tiancheng zhao, ran zhao, and maxine eskenazi. 2017. learning discourse-level diversity for neural dialog models using conditional variational autoencoders. in proceedings of the 55th annual meeting of the association for computational linguistics (volume 1: long papers), volume 1, pages 654-664. xianda zhou and william yang wang. 2018. mojitalk: generating emotional responses at scale. in proceedings of the 56th annual meeting of the association for computational linguistics (volume 1: long papers), pages 1128-1137. association for computational linguistics.