PEDN et: A Persona Enhanced Dual Alternating Learning Network for Conversational Response Generation (2020)

Abstract: Endowing a chatbot with a personality is essential to deliver more realistic conversations. Various persona-based dialogue models have been proposed to generate personalized and diverse responses by utilizing predefined persona information. However, generating personalized responses is still a challenging task since the leverage of predefined persona information is often insufficient. To alleviate this problem, we propose a novel Persona Enhanced Dual Alternating Learning Network (PEDNet) aiming at producing more personalized responses in various open-domain conversation scenarios. PEDNet consists of a Context-Dominate Network (CDNet) and a Persona-Dominate Network (PDNet), which are built upon a common encoder-decoder backbone. CDNet learns to select a proper persona as well as ensure the contextual relevance of the predicted response, while PDNet learns to enhance the utilization of persona information when generating the response by weakening the disturbance of specific content in the conversation context. CDNet and PDNet are trained alternately using a multi-task training approach to equip PEDNet with the both capabilities they have learned. Both automatic and human evaluations on a newly released dialogue dataset Persona-chat demonstrate that our method could deliver more personalized responses than baseline methods.

## introduction generating human-like conversations by chatbots has been a long-term goal in artificial intelligence. end-to-end neural generative models attract much attention as potential solutions to open-domain dialogue systems, which aim to conduct smooth and natural conversations and gain trust from users. the sequence-to-sequence (seq2seq) models @xcite @xcite have achieved success in generating semantic and syntactic responses for a given conversation context. nevertheless, seq2seq tends to generate short and generic responses like "i don't know" and "i'm fine" as they predict the response by using the maximum-likelihood estimation (mle) objective function @xcite . and the generated responses are often lack of consistent personality since seq2seq is trained over collected conversations from different speakers @xcite . to tackle these issues, various methods have been proposed toward diverse and consistent response generation, by endowing open-domain chatbot with a coherent persona. for example, @xcite introduced persona profiles in response generation where the persona information is used to enrich responses, and their experiments demonstrated improvements over those not leveraging persona information. @xcite further proposed a persona selection method, which focused on selecting the optimal persona to generate personalized responses efficiently. these efforts have obtained considerable attention and proven that leveraging the additional persona information can promote more user engagement in open-domain dialogue systems. in this paper, we propose a novel persona enhanced dual alternating learning network (pednet) to achieve an appropriate and rich persona expression in responses. pednet is composed of a context-dominated network (cdnet) and a persona-dominated network (pdnet). they are built upon a common encoder-decoder backbone with shared parameters. specifically, cdnet is a general persona-based dialogue model equipped with a memory network @xcite which is capable of reading and conditioning on persona profile. it is reinforced with a persona-aware representation to select persona properly. different from cdnet, pdnet generates responses by conditioning on the optimal persona pre-labeled by bert @xcite and a general representation of the conversation context that contains no specific content (e.g., "do you see...today?" of context x1 in the main contributions of this work are summarized as follows: the success of seq2seq motivates the development of the research for improving the quality of response generation. however, the problem of tending to generate generic or inconsistent responses still remains in this domain. one promising approach to alleviate such a challenge is to find a better objective function @xcite . besides, learning the inherent attributes is another way to improve the diversity of responses and ensure the contextual consistency. @xcite first proposed a persona-based model by using user embeddings, which projects each user into a vector and feeds the vector to the decoder at each decoding step. @xcite employed a two-phase training approach, which initialized the model using large scale data and then fine-tuned the model to generate personalized responses. @xcite explored a response generation model conditioned on both speakers and context history. nevertheless, these models cannot explain how the personality expressed in the response is captured since all the information about the user is encoded as a dense vector for decoding. to maintain a coherent personality, @xcite designed a model explicitly expressing a profile value in the response according to its prespecified persona profile. @xcite created a personachat dataset, and they proposed two generative models to incorporate persona information into responses. @xcite used a copy mechanism which allowed the decoder to hierarchically attend and copy from the external persona profile in addition to the conversation context. a persona selection mechanism was proposed in @xcite , where both prior and posterior distributions over persona are used to facilitate persona selection. @xcite proposed a memory-augmented architecture to exploit persona information from context and incorporate a conditional variational autoencoder model together to generate diverse responses. so far, the studies all generate responses conditioning on both the context and the persona without distinction and emphasis, which often result in insufficient utilization of persona information. different from those previous works, we design a dual alternating learning network, which explicitly distinguishes the different effects of specific content in the context on the two aspects of persona selection and persona incorporation, so as to realize an effective persona expression. ## conclusion and future work in this paper, we focus on the task of personalized response generation and propose the persona enhanced dual alternating learning network to improve the explicit expression of personality. two networks that learn through a multi-task training approach are designed to select the appropriate persona according to the conversation context, as well as enhance the utilization of persona information in response generation. experimental results on both automatic and human evaluations verify the effectiveness and usefulness of our model. for future work, we plan to consider the long-distance conversation history to improve the persona selection and extend our persona integration method in multi-turn conversations.