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 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 method outperforms existing KGC methods significantly on both automatic evaluation and human evaluation. Open-domain dialogue system often suffers from safe response (Li et al., 2015; Zhang et al., 2019) 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 (Zhou et al., 2018a; Dinan et al., 2019; Zhao et al., 2020a), 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. * Corresponding author: Rui Yan (ruiyan@ruc.edu.cn) generate informative and engaging responses. As external knowledge supplements the background to the inputs and decides what to say, knowledge selection is a key ingredient in KGC. Numerous methods have been developed to tackle the knowledge selection problem by sequential latent variables (Kim et al., 2020; Meng et al., 2020), reinforcement learning (Zhao et al., 2020b), or expectation maximization algorithm (Li et al., 2020). 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 (Dinan et al., 2019) and other benchmarks in KGC (Gopalakrishnan et al., 2019), 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 na-ture. 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. Though it has been discussed in open-domain dialogue (Li et al., 2016; Zhang et al., 2018), no previous research sheds light on the personalization issue in KGC and there exists no dialogue dataset featured with external facts and personal memory at the same time. Besides, there is no annotated label to indicate which knowledge candidate a person will choose based on his or her personal memory. Namely, the mapping between personal memory and knowledge selection is highly unconstrained without golden label. Intuitive resolution like treating personal memory as additional knowledge is sub-optimal because of dependency between knowledge and personal memory, as is shown in our experiments. 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 reconstruc-tion 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: (1) We explore the personalization issue of the knowledge selection task in KGC and construct a dataset featured with user-specific personal memory to benefit relevant research in the future. We are the first to explore the possibility of introducing personal memory into KGC. (2) We propose a novel variational method and introduce two latent variables to model the interdependency between the persona and knowledge. Besides, we employ dual learning to optimize the relationship between the dialogue context, personal memory and knowledge in a unified framework. (3) We conduct extensive experiments and verify the proposed methods empirically. Both the automatic and human evaluation evidence the efficacy of our proposed method. There is a substantial literature in the field of knowledge-grounded conversation. With the grounding of external knowledge in format of knowledge graph (Zhou et al., 2018a; Wu et al., 2019), document (Ghazvininejad et al., 2018; Zhou et al., 2018b; Zhao et al., 2019) or visual background (Das et al., 2017), 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 (Kim et al., 2020). 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 (Ghazvininejad et al., 2018), sequential latent variables (Kim et al., 2020; Meng et al., 2020), reinforcement learning (Zhao et al., 2020b) and so on. A recent work gives attention to the expression style of knowledge (Zhao et al., 2021). However, they only focus on the decoding phase and no methods shed light on the personalization issue of knowledge selection, to our best knowledge. (2016), dual learning is a semi-supervision learning scheme aiming at utilizing large-scale unlabeled data. Together with its newly appeared variants in recent years (Xia et al., 2017, 2018; Wang et al., 2019), dual learning has been successfully applied in neural machine translation (Xia et al., 2017; He et al., 2017), image-image-translation (Yi et al., 2017; Lin et al., 2018), sentiment analysis (Xia et al., 2017), automatic speech recognition (Ren et al., 2019), question answering (Tang et al., 2017), and knowledge-grounded dialogue (Meng et al., 2020). Our work is related to dual learning as well. First proposed in neural machine translation by He et al. (2016), 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. Suppose we have a KGC dataset D with N case, and every case is in format of ( C, K , R ) , where C = [ u 1 , u 2 ,    , u l C ] is the context of the dialogue with l C tokens in total, K = { K 1 , K 2 ,    , K |K| } is a set of |K| knowledge candidates. And R = [ r 1 , r 2 ,    , r l R ] is a response in this conversation corresponding to a specific user with unique user id. Different from the original KGC task, we have a memory repository M . For every interlocutor corresponding to the response, a set of his or her personal memory P = { P 1 , P 2 ,    , P |P| } composed of |P| customized utterance history could be retrieved from the memory repository. Our goal is to learn a probabilistic model p ( R | C, K , P ) that could generate a personalized and informative response based on personal memory and knowledge. Figure 2 gives a graphical model of our methods. As is shown, the core of our proposed method is five probabilistic models to calculate the prior and posterior distribution of Z p , Z k and an auxiliary distribution of Z p . During training, we devise an unsupervised learning scheme, in which we optimize the distribution of two latent variables Z p and Z k by dual learning. To be more specific, we first sample a  Z p from the posterior distribution q  ( Z p | C, R ) , and then calculate the forward map         distill dual condition KL-div KL-div dual loop VAE    , ,   ) (  |, ,   ) (  |)    , ,   ) (  |,   ) Figure 2: A graphical representation of our proposed method. ping from memory to knowledge q  ( Z k | C, R,  Z p ) , from which we sample a  Z k . The reward is designed as the probability of reconstructing the selected memory fragment by the auxiliary distribution   ( Z p =  Z p | C, R,  Z k ) . By maximizing the reward, the primal task and the auxiliary task could benefit each other. The gains of the auxiliary distribution is distilled to q  ( Z p | C, R ) , such that the two posterior distribution and the auxiliary distribution form a closed loop. Besides, the prior distribution is forced to get close to the posterior distribution via KL-divergence. In the inference phase, the prior distribution of Z p is calculated at first, from which we sample and activate a personal memory fragment. After that, the woken memory fragment is used to decide the prior knowledge distribution p  ( Z k | C ) . Finally, the knowledge sampled from Z k together with the memory fragment is sent into a generator to synthesize a response. Note that the golden response is only involved in the training phase. ,  and  are all learnable parameters. To make the latent variables interpretable, we set the latent space of Z p and Z k as the number of memory fragments or knowledge candidates to choose from, and each sampling corresponds to a single piece of memory fragment or a knowledge candidate. Furthermore, motivated by human cognitive process, the aroused personal memory fragment implies one's preference for different external knowledge, which influences the likelihood of choosing different knowledge. In light of this, 3903 the prior distribution of ( Z p , Z k ) is factorized as: p ( Z p , Z k ) = p ( Z k | Z p ) p ( Z p ) (1) And to calculate their probability distribution, we adopt BERT (Devlin et al., 2018) as the backbone of our method to obtain a dense representation of dialogue context, response, candidate knowledge sentence or personal memory fragment. Take the calculation of the prior distribution p  ( Z k | C, Z p ) as an example. We first concatenate the context C , the memory fragment P indicated by the sampled Z p , and the i -th candidate knowledge K i together as a long sequence. A special [CLS] token is prepended at the beginning of the sequence and [SEP] is inserted to separate different utterances: I = u 1 , u 2 ,    u l C , p 1 , p 2 ,    , p l P , k 1 , k 2 ,    k l Ki , (2) where l C , l P and l K i are the number of tokens in the context, memory facet and knowledge candidate respectively. Then the embedding layer will convert I into input representations, which is the sum of the corresponding token embedding and position embedding. Thereafter, the BERT encoder performs multi-head attention on the input representation to obtain a dense representation. There are n identical layers in the BERT encoder, and for each layer, the multi-head attention could be formulated as H l = FFN(MultiHead( Q l  1 , K l  1 , V l  1 )) , (3) where FFN(  ) is a feed-forward network and we use Q l  1 , K l  1 , and V l  1 to denote the query matrix, key matrix and value matrix after the l  1 th layer respectively. For self-attention, we have Q l  1 = K l  1 = V l  1 = H l  1 , (4) where H l means the hidden state at the l -th layer. Specially, H 0 is the input embedding and H n is the final output of the BERT. We use the vector corresponding to the position of the special [CLS] token in H n as the representation of the i -th knowledge candidate, which is referred to as h i . Then the distribution of Z k is calculated as p  ( Z k = i | C, Z p ) = exp( f ( h i )) |K| (cid:80) j exp( f ( h j )) , (5) where f (  ) is a multi-layer perceptron. The prior and posterior distribution of Z k and Z p are calculated in a similar way. The only difference lies in the constitution of input sequence I : For the prior distribution of Z p , I is the concatenation of dialogue context and a candidate personal memory facet: I = u 1 , u 2 ,    u l C , p 1 , p 2 ,    , p l P (6) And to calculate the posterior distribution, we insert the response tokens behind the dialogue context tokens as the response usually contains clue indicating the selected knowledge and memory. Namely, to compute q  ( Z p | C, R ) , the posterior of Z p , the input is: I = u 1 , u 2 ,    u l C , r 1 , r 2 ,    , r l R , p 1 , p 2 ,    , p l P (7) And for q  ( Z k | C, R, Z p ) : I = u 1 , u 2 ,    u l C , r 1 , r 2 ,    , r l R , p 1 , p 2 ,    , p l P , k 1 , k 2 ,    , k l K (8) Normally, the generator g of our method could be specified as any large-scale pre-trained language model. Here we define the generator as GPT-2 (Radford et al., 2019). Previous methods often synthesize a response merely based on the dialogue context and the selected knowledge, taking no consideration of the persona of the interlocutor, which may lead to an inconsistency in persona. Different from that, we input the sampled personal memory fragment and the sampled knowledge candidate into GPT-2 all together with the dialogue context. Intuitively, personal memory fragment implies why the knowledge is paid attention to and underlying relevance between the persona of the interlocutor and the knowledge, which endows the generator to generate persona-consistent and knowledgeable responses: g ( R ) = g ( R | C, Z p , Z k ) = l R (cid:89) i =1 g ( r i | C, Z p , Z k , r <i ) (9) 3.4 Learning Details Directly maximizing the marginal log-likelihood of generating the correct response g ( R | C, Z p , Z k ) requires integrating over all possibilities of Z k and Z p , which is more than time-consuming. Inspired by variational inference, we introduce a variational posterior as the true posterior is intractable. Thereby, instead of directly optimizing 3904 Algorithm 1 The proposed learning algorithm. 1: Input: Training KGC dataset D , memory repository M 2: Warm up p  ( Z p ) , p  ( ZK | Z p ) , q  ( Z p | R ) and q  ( Z k | R, Z p ) on D . 3: while not converge do 4: Sample a mini-batch { ( C, K , R ) } from D . 5: Retrieve the user-specific personal memory P from the memory repository. 6: Calculate the prior personal memory distribution p  ( Z p ) with C . 7: Sample a Z p and then calculate the prior distribution of knowledge p  ( Z k | Z p ) . 8: Calculate the posterior memory distribution q  ( Z p | R ) based on C and R , and then sample a  Z p from that. 9: Calculate the posterior knowledge distribution q  ( Z k | R,  Z p ) , and then sample a  Z k from that. {The primal task} 10: Compute the reward Re 1 as the Reconstruct probability   ( Z p =  Z p | Z k ) . 11: Update  according to Eq. 16. 12: Calculate the auxiliary memory distribution   ( Z p | R,  Z k ) based on the pseudo knowledge label  Z k , and sample a  Z p from that.{The dual task} 13: Compute the reward Re 2 as q  ( Z k =  Z k |  Z p ) . 14: Update  according to Eq. 15. 15: Update  according to Eq. 10. 16: Update  according to Eq. 17. 17: end while 18: return The prior distribution p  ( Z p ) and p  ( ZK | Z p ) the marginal log-likelihood, we derive an evidence lower bound objective to maximize: LELBO = E q  ( Z k | Z p ) q  ( Z p ) g ( R | C, Z p , Z k )  E q  ( Z p ) KL ( q  ( Z k | Z p ) || p  ( Z k | Z p ))  KL ( q  ( Z p ) || p  ( Z p )) (10) where q  ( Z k | Z p ) , q  ( Z p ) , p  ( Z p ) , p  ( Z k | Z p ) are shorthand for q  ( Z k | C, R, Z p ) , q  ( Z p | C, R ) , p  ( Z p ) and p  ( Z k | C, Z p ) respectively. A stepwise derivation could be found in the supplementary materials. The forward mapping from personal memory to knowledge candidates is relatively implicit and obscure, partially because the customized utterance history contains unwanted noise. As a result, there is a tendency that Z p is ignored and p  ( Z k | Z p , C ) is degenerated into p  ( Z k | C ) , which we refer to as the vanishing memory . To address this issue, inspired by the idea of dual learning (He et al., 2016), we introduce an inverse mapping from knowledge candidate to personal memory as a dual task, which is depicted by the auxiliary distribution   ( Z p | C, R, Z k ) . Intuitively, there is a natural duality between the mapping from personal memory to knowledge and the inverse mapping. Therefore, if the forward mapping makes a good inference about the knowledge to choose, the inverse mapping is able to map it back to personal memory, which means that the memory is not vanishing. And before the dual learning procedure, the primal task and the dual task are warmed up to speed up convergence and alleviate error accumulation in the dual learning process, following the idea of He et al. (2016) and Meng et al. (2020). Namely, we construct pseudo knowledge label  P and persona label  K based on their similarity to the response. K = max K i K Sim ( K i , R )  P = max P i P Sim ( P i , R ) (11) Then, both the primal task and the dual task are warmed up with a traditional maximum likelihood estimation objective. After the warm-up procedure, for each iteration, we first sample a  Z p according to its posterior distribution q  ( Z p | C, R ) . Then the forward mapping calculates the probability distribution q  ( Z k | C, R,  Z p ) , from which we sample a  Z k . The reward for the forward mapping is defined as the probability that the auxiliary distribution recovers the  Z p . Mathematically, we have Re 1 =   ( Z p =  Z p | C, R,  Z k ) (12) Symmetrically, the reward for the auxiliary distribution is the prediction probability of the golden knowledge by the forward mapping: Re 2 = q  ( Z k =  Z k | C, R, Z p ) , (13) where  Z k is corresponding to the pseudo knowledge label. And the objective of the dual learning is to maximize the reward: L dual = ED [ Re 1 + Re 2 ] (14) For reward maximization, we optimize the parameter through policy gradient method (Sutton et al., 2000):   L dual =   log   ( Z p =  Z p | C, R,  Z k ) Re 2 . (15)   L dual =   log q  ( Z k =  Z k | C, R,  Z p ) Re 1 . (16) Finally, the gains of the dual task is distilled into the posterior distribution of Z p via a cross-entropy loss: L dis =  KL (  T ( Z p | C, R, Z k ) || q T ( Z k | C, R, Z p )) +  log q  ( Z p =  Z p | C, R, Z k ) , (17) 3905 where  is a hyper-parameters to balance the weights of two parts and the superscript T means that the distribution is normalized at temperature T . Thus, the three probabilistic models form a closed loop in which each component is trained alternatively. The full procedure of our proposed learning algorithm is concluded in Algorithm 1. 4 Experiment 4.1 Dataset Since existing dataset like CMU_DoG (Zhou et al., 2018b) or Holl-E (Moghe et al., 2018) do not contain information about personal memory, we establish a new KGC dataset equipped with a memory repository. The dataset is constructed based on Reddit (Baumgartner et al., 2020). In detail, we download the conversational data on the PushShift dump of Reddit ranging from 2011 to the first half of 2015 and divide them into a training set, a validation set and a test set according to the date. To construct a memory repository, we maintain a dictionary where the key is a long string hashed from the user account name and the value is a set of utterances of the user. Since it is a repository for user-specific utterances, it may inevitably contain false beliefs or subjective opinions. We shall leave this issue for future work. Elaborated data filtering is conducted to ensure: (1) We only keep utterances from users that have at least 5 utterances in the memory repository; (2) Utterances that are too long or too short are filtered; (3) Paraphrase tool (Damodaran, 2021) is applied on every utterances to avoid tracing the utterances back to real reddit users. The statistics of our dataset is shown in Table 1. And the code is available at https://github. com/Lucasftc/PersonaKGC . A few examples is shown in Appendix A.3. To benefit future research and meanwhile avoid possible malicious abuse, the dataset is available upon request from the authors 1 . To verify the effectiveness of the proposed methods, we compare our methods with baselines in KGC. Meanwhile, since our proposed method makes use of personal memory to generate persona-consistency response, we also compare our methods with baselines in personalized dialogue. Generative Profile Memory Network (GPMN) (Zhang et al., 2018) is a method in personalized dialogue which employs Memory Network along with persona information. Transformer Memory Network (TMN) (Dinan et al., 2019) adopts the traditional Memory Network with transformer architecture and introduces the knowledge selection loss. Transfertransfo (Wolf et al., 2019) is a combination of a transfer learning based training scheme and a high-capacity transformer model and achieves the best results in the Conversational Intelligence Challenge 2.  Sequential Knowledge Transformer (SKT) (Kim et al., 2020) utilizes sequential latent variables for knowledge selection. We use the pseudo knowledge labels for the golden knowledge label in implementation. KnowledGPT (Zhao et al., 2020b) puts the knowledge selector and the response generator in a framework and employ reinforcement learning and curriculum learning to accomplish the state-of-the-art performance in KGC. KnowledGPT+M , a variant of KnowledGPT where we treat personal memory as knowledge candidates as well and input them to the knowledge selector. P 2 BOT (Liu et al., 2020) is a transmitter-receiver based framework explicitly modeling the perception between the interlocutors and achieves the state-of-the-art in personalized dialogue. BoB (Song et al., 2021) is a newly published method that disentangles personalized dialogue into persona understanding and personalized generation. We choose distinctness, BLEU(Papineni et al., 2002), ROUGE(Lin, 2004) 2 and ME-TEOR(Denkowski and Lavie, 2014) 3 to be our automatic metrics. Focusing on the exact n-gram co-occurrence in hypothesis and reference, BLEU and ROUGE evaluate the appropriateness of the proposed model. Distinctness is calculated as the ratio of unique unigrams and bigrams, paying more attention to the diversity of generated text. METEOR measures the alignment, or the exact, stem, synonym, and paraphrase matches between the hypothesis and reference. Apart from automatic evaluation, we conduct human evaluation. Specifically, 200 examples are randomly sampled from the test set and well-educated native speakers are recruited to assess the quality of the generation from different models with their source hidden. Each annotators are required to give a score in { 0 : bad , 1 : fair , 2 : good } for three independent aspects: (1) fluency : whether the reply is fluent; (2) coherence : whether the reply is coherent with the context; and (3) faithfulness : whether the reply is well-grounded and faithful to the selected knowledge sentence and memory fragment. The agreement of annotators is measured via Fleiss' kappa (Fleiss, 1971). We first report the experimental result in automatic evaluation. As is shown in Table 2, our method outperforms the state-of-the-art baselines in KGC and personalized dialogue in most metrics, verifying the effectiveness of our model empirically. Among non-pretrained methods, TMN and GPMN are low in diversity, since their generator is not pre-trained on large corpus before. SKT improves distinctness but shows low appropriateness, possibly because that it highly relies on the golden knowledge label, which is costly and not always available. In pre-trained based methods, Transfertransfo attains impressive results on distinctness. It also achieves competitive appropriateness results, but not as good as ours. We gauge the performance of the model to the large document-level training corpus, a critical choice for pre-trained language model, which may boost the diversity of generated text. Besides, the performance of the BoB, a recently published baseline, is less satisfactory compared with others. The premise of BoB is the disentanglement between contextual coherence and persona consistency, which is not always achievable especially when we use user-specific dialogue history for personal memory information. And it is notable from the table that there is a significant gap between the baseline methods in KGC or personalized dialogue and ours, validating that neither simply projecting personal information into dialogue nor purely grounding on knowledge is an acceptable solution to the KGC task. It is necessary to combine personal memory and external knowledge together. The comprehensive improvement of KnowledGPT+M in contrast with the original KnowledGPT also reveals this viewpoint. Additionally, the considerable advantage of our proposed method over KnowledGPT+M illustrates the fact 3907 Models BLEU ROUGE Distinct METEOR B-1 B-2 B-3 B-4 R-1 R-2 R-3 D-1 D-2 BoB 4.69 1.57 0.65 0.31 10.68 1.57 9.30 4.94 17.06 3.97 w/o. that treating personal memory as knowledge is not enough. The dependency between personal memory and the knowledge should not be ignored. We also present the result of human evaluation since no automatic metric is perfect in this task (Di-nan et al., 2019). Since human evaluation is time-consuming and expensive, only competitive baselines are involved. As shown in Table 3, our proposed model outperforms the baseline methods and there is an evident improvement. Apart from the main results, we are especially interested interested in some research questions:  (RQ1) How does each component contributes to the performance of our model? (RQ2) How many knowledge sentences and memory fragments to select? There is 3908 Recall@1 Recall@2 Recall@5 Recall@10 m=1 0.173 0.286 0.505 0.720 m=2 0.176 0.289 0.513 0.730 m=3 0.177 0.289 0.509 0.730 m=4 0.176 0.288 0.508 0.730 Table 5: The performance of p  ( Z k | C, Z p ) under different m . To answer the first question, we conduct ablation study and compare the full model with several vari-ants:(1) w/o. know . the external knowledge base to grounding the dialogue is removed; (2) w/o. mem . personal memory is removed and this variant is a standard KGC model essentially; (3) w/o. dual . the dual task is removed, so there is no dual learning and distillation in this variant; (4) w/o. dep . the dependency of the two latent variables is removed so Z p and Z k are calculated independently. The ablation result is shown in Table 4, from which we could have the following observations: (1) w/o. know and w/o. mem exhibit a degeneration at a great extent, further justifying the necessity of introducing knowledge and personal memory into a dialogue system, respectively. (2) w/o. dep also shows an obvious deterioration. This is in line with our expectation since w/o. dep model Z k and Z p as two independent latent variables, ignoring the underlying dependence between them. Comparatively speaking, w/o. dual achieves a better result, but not as good as the full model due to the destroy of the closed dual loop. And to have a intuitive perception about the effect of the closed dual loop, we examine the promotion brought to the q  ( Z k | C, R, Z p ) ,   ( Z p | C, R, Z k ) and q  ( Z p | C, R ) in terms of Re-call@1 of knowledge or personal memory. The result is shown in Figure 3. From the figure we could see that there is an obvious improvement after trained with our proposed learning algorithm. For the (RQ2) , we first explore it by varying the amount of selected personal memory fragments and observe how the knowledge selection procedure is influenced. In detail, we vary the number of personal memory fragments m sampled by p  ( Z p | C ) from 1 to 4 and evaluate the performance of p  ( Z k | C, Z p ) in terms of Recall@n (n  {1,2,5,10}). a fluctuation or slight drop when m continues to increase possibly owing to the distraction mixed with the redundant personal memory. Besides, we are also curious about the final generation performance under different numbers of knowledge and personal memory fragment. It could be seen from Figure 4 that there appears a decline when we increase the number of knowledge and personal memory fragment, which we attribute to the unwanted noise mixed with personal memory and knowledge. 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. Intended Use The chief purpose of our dataset is to examine a dialogue model's capacity in selecting proper knowledge with the help of personal memory. The dataset is mainly for research propose and it is not supposed to be directly used to train a production system. And researchers should be aware of the possible ethic issues before exploiting our dataset. Data Collection All the examples in our dataset are in English and no human annotators are involved in the data collection process. As mentioned in Sec.4.1, our dataset is built on the basis of the Reddit dumps from Pushshift (Baumgartner et al., 2020), which is a publicly available resource widely used in more than a hundred peer-reviewed publications. Our data collection is in consistent with the term of use and the research is granted ethical approval by an external institutional review board. To avoid potential abuse, the dataset is available upon request to the authors. Contact the authors (by email) and clearly state your intended use if you believe the dataset might be helpful in your research. User Privacy Although our dataset includes user-specific utterance history as personal memory, no user account names will be revealed or inferred from the dataset. Besides, the utterance histories are paraphrased during our procession of the dataset such that they can not be traced back to the real users in Reddit. In conclusion, There is no personally identifiable information in our dataset or underlying leakage of personal information. Thanks for the reviewers for their valuable suggestions. This work was supported by National Natural Science Foundation of China (NSFC Grant No. 62122089 & No. 61876196 & No. 61832017), Beijing Outstanding Young Scientist Program NO. BJJWZYJH012019100020098, and Intelligent Social Governance Platform, Major Innovation & Planning Interdisciplinary Platform for the "Double-First Class" Initiative, Renmin University of China. We also wish to acknowledge the supports provided and contributions made by Public Policy and Decision-making Research Lab of RUC, and the Public Computing Cloud, Renmin University of China. Rui Yan is also supported by Beijing Academy of Artificial Intelligence (BAAI).
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