There Are a Thousand Hamlets in a Thousand People’s Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory (2022)

Abstract: Knowledge-grounded conversation (KGC) shows great potential in building an engaging and knowledgeable chatbot, and knowledge selection is a key ingredient in it. However, previous methods for knowledge selection only concentrate on the relevance between knowledge and dialogue context, ignoring the fact that age, hobby, education and life experience of an interlocutor have a major effect on his or her personal preference over external knowledge. Without taking the personalization issue into account, it is difficult for existing dialogue systems to select the proper knowledge and generate persona-consistent responses. In this work, we introduce personal memory into knowledge selection in KGC to address the personalization issue. We propose a variational method to model the underlying relationship between one’s personal memory and his or her selection of knowledge, and devise a learning scheme in which the forward mapping from personal memory to knowledge and its inverse mapping is included in a closed loop so that they could teach each other. Experiment results show that our methods outperform existing KGC methods significantly on both automatic evaluation and human evaluation.

## introduction open-domain dialogue system often suffers from safe response @xcite problem as they could only refer to the context when generating a response. to alleviate this, knowledge-grounded conversation (kgc) is proposed to introduce external fact and real-world commonsense as prior knowledge @xcite @xcite , such that a dialogue system is able to ground the conversation with the provided knowledge and therefore † the first two authors contribute equally. xueliang zhao is responsible for the design of the methodology and algorithm. tingchen fu is responsible for the implementation and experiment. the order is decided by a coin flip. numerous methods have been developed to tackle the knowledge selection problem by sequential latent variables @xcite , reinforcement learning @xcite , or expectation maximization algorithm @xcite . in spite of the progress in this task, knowledge selection remains an unsolved problem as the precision is still far from satisfactory in wizard of wikipedia @xcite and other benchmarks in kgc @xcite , which also hinders the optimization of subsequent response generation models. a crucial point is, they often make assumption that the golden knowledge is distinguishable as long as the dialogue context is known, yet this is not always held true because there exists a one-to-many relationship in conversation and the past utterance history in a dialogue session is insufficient to decide the knowledge selection or the future trend of a dialogue. as is shown in figure 1 , personalization is a key to success in the task because knowledge selection is a personal or subjective process in nature. when people communicate with each other, their perception of dialogue context will evoke their past memory about relevant life experience, taste and values, which we refer to as personal memory. the aroused fragment of personal memory further guides their interest and preference for different knowledge. motivated by this, we postulate a new task named personalized kgc, introducing personalization into knowledge-grounded dialogue to encourage more human-like knowledge selection. importing persona memory into knowledge selection is a non-trivial task. one of the challenge is concretization of personal memory. personal memory is an abstract concept related to user-specific experience, which is difficult to depict or model. hey, you know beckett ? to address the above issue, we construct a kgc dataset featured with personalized memory repository, collecting user-specific utterance history under multiple types of context, which is a reflection of one's personal memory. and to discover the underlying relationship between the dialogue context, personal memory and knowledge, we propose a variational method and introduce two latent variables z p and z k to indicate the fragment of personal memory to evoke and the knowledge candidate to select respectively. and to model the mapping from z p to z k , we introduce an inverse mapping as a dual task and employ dual learning to allow the two mappings to teach each other. the motivation behind this is intuitive: the reconstruction of personal memory from selected knowledge candidate is natural and easy if the mapping from personal memory to knowledge is accurate. extensive experiment shows that our methods outperform competitive baselines in both automatic evaluation and human evaluation, justifying the importance of introducing personal memory and the effect of the dual learning mechanism empirically. the contributions of this work are three-fold: ## related work there is a substantial literature in the field of knowledge-grounded conversation. with the grounding of external knowledge in format of knowledge graph @xcite , document @xcite @xcite or visual background @xcite , it is regarded as a critical method towards intelligent dialogue system. nowadays, existing methods in kgc often share a paradigm that decomposes the task into two related sub-problems, namely knowledge selection and utterance generation @xcite . in this work, we mainly focus on the knowledge selection task. to this end, a great deal of methods have been proposed to retrieve the most relevant knowledge by memory network @xcite , sequential latent variables @xcite , reinforcement learning @xcite and so on. a recent work gives attention to the expression style of knowledge @xcite . however, they only focus on the decoding phase and no methods shed light on the personalization issue of knowledge selection, to our best knowledge. our work is related to dual learning as well. first proposed in neural machine translation by @xcite , dual learning is a semi-supervision learning scheme aiming at utilizing large-scale unlabeled data. together with its newly appeared variants in recent years @xcite @xcite , dual learning has been successfully applied in neural machine translation @xcite , image-image-translation @xcite , sentiment analysis @xcite , automatic speech recognition @xcite , question answering @xcite , and knowledge-grounded dialogue @xcite . our work is related to dual learning as well. first proposed in neural machine translation by @xcite , dual learning is a semi-supervision learning scheme aiming at utilizing the large scale unlabeled data. in this work, we apply dual learning to model the inter-dependency relationship between one's personal memory and his or her choice of knowledge. ## conclusion in this work, we explore personalized kgc by introducing personal memory into knowledge selection task. two latent variables are introduced to select knowledge and personal memory respectively. besides, dual learning scheme is employed to allow the two selection task to teach each other. for future work, we would like to extend the personalized knowledge-grounded dialogue to personalized conversational recommendation system for application in online shopping.