What, When, and How to Ground: Designing User Persona-Aware Conversational Agents for Engaging Dialogue (2023)

Abstract: This paper presents a method for building a personalized open-domain dialogue system to address the WWH (WHAT, WHEN, and HOW) problem for natural response generation in a commercial setting, where personalized dialogue responses are heavily interleaved with casual response turns. The proposed approach involves weighted dataset blending, negative persona information augmentation methods, and the design of personalized conversation datasets to address the challenges of WWH in personalized, open-domain dialogue systems. Our work effectively balances dialogue fluency and tendency to ground, while also introducing a response-type label to improve the controllability and explainability of the grounded responses. The combination of these methods leads to more fluent conversations, as evidenced by subjective human evaluations as well as objective evaluations.

## introduction a personalized dialogue (pd) system is capable of generating user-customized responses based on long-term memory about the user's persona, leading to more trustworthy and engaging conversations @xcite . in our study, persona attributes cover comprehensive user-related information, such as personality, behaviors, preferences, and experience. the key to enhanced user engagement in a pd system lies in finding a persona that is contextually relevant and appropriate, on which a model is grounded to generate a natural response. however, as shown in the example in figure 1 , pd systems usually need to select relevant persona attributes from a given subset of n persona attributes, which is usually provided by external memory or retrieved from the user persona pool. considering the fact that the agent's response is usually annotated with associated oracle persona information figure 1 : a sample of personalized conversation grounded on user persona. for every agent utterance, the persona attributes to be grounded in the response are retrieved by a retrieval model. then, the agent make a decision about generating personalized response given dialogue context and retrieved persona subset. in the training dataset, deciding what persona attribute to select in each turn during model inference is a non-trivial problem. (we will refer to this problem as the "what to ground" problem hereafter.) another aspect to consider in a pd system is that under certain dialogue contexts, it is better not to generate a personalized response given retrieved persona attributes in order to create a more natural interaction (the second response in figure 1 ). as shown in the example, it is usually difficult for the retrieval module to determine whether to use persona information for response generation. we also need a model to decide when to ground persona information with a given persona subset for every turn (we will call this the "when to ground" problem hereafter). given such a challenge, designing a user-based persona-aware pd system capable of generating engaging and human-like personalized responses requires addressing the "what," "when," and "how" (wwh) questions: 1) what personal information should be grounded given the conversation context, 2) when to generate responses using personal information, and 3) how to make natural and human-like personalized response. large-scale language models (llms) such as gpt-3 have shown outstanding capabilities in various natural language understanding (nlu) tasks and especially, in-context learning @xcite . however, the inherent abilities of llms alone are insufficient to effectively address the wwh problems in real-world service environments. moreover, it is very tricky to generate natural and engaging personalized responses and sophisticatedly control the output of the model in multi-turn/session scenarios, relying solely on prompt engineering. in addressing the research gap and real-world challenges, we propose a method that controls the inclination of models to generate personalized responses. our technique blends persona-augmented datasets to construct a personalized dialogue system, thus enabling human-like natural conversations. our approach involves the following steps: we also propose four grounding type categorizations to allow for analysis of the model's grounding patterns and detailed performance in subjective evaluation using sensibleness and specificity, which complements the objective evaluation based on groundedness, and fluency. ## related work since the release of the personachat dataset @xcite , methods to generate personalized responses that are consistent with or grounded on a persona have been extensively studied @xcite @xcite . most of the studies focus on addressing the what and how challenges by the use of diverse model architectures, modules, and training frameworks @xcite . liu et al. (2020) propose an rl-based approach for generating personalized dialogue with rewards for mutual persona perception. wu et al. (2019) and fu et al. (2022) employ variational methods to generate personalized and knowledgeable response generation. song 2 conversation (episode -4 sessions) session 4 session 3 session 2 u: 오늘따라 예약이 많네... (there are so many reservations today...) a: 예약이라면 네일아트 예약을 말하는 거야? [𝝆𝟒] (you mean nail art reservations? ) u: 응 너무 바빠서 밥 먹을 시간이 없을 것 같아 ㅠ (yeah, i'm too busy to have lunch :( ) a: 그럼 점심으로 간단하게 뭐라도 시키는 건 어때? (how about ordering some food for a quick lunch? ) u: 괜찮은 생각이네! 지금 주문해야겠다. (that's a good idea! i should order it right now.) a: 너 엄청 배고플텐데 얼른 주문해! ( you must be really hungry. so order quickly!) u: 응. 내가 좋아하는 떡볶이도 같이 시켰어! ㅋ (yeah. i also ordered tteokbokki, which is my favorite! lol) [𝝆𝟔] a: 맛있겠다~ 퇴근하고는 뭐 할거야? (that sounds great~ what are you gonna do after work? ) u: 넷플릭스나 좀 보고 있을려고!! (i'm planning to watch netflix!!) a: 로맨스 영화 볼꺼야? ( are you going to watch a romance movie? ) [𝝆𝟏] u: 아니, 요새 미드에 빠졌어 ㅋㅋ 여튼 얼른 먹고 다시 일하러 가야겠다! [𝝆𝟕] (no, i've been hooked on american dramas lately. anyway, i'll quickly eat and get back to work! ) a: 맛있게 먹고 남은 하루도 힘내! (enjoy your meal and have a great rest of the day!) session 1 user persona attributes user demographic information age: 20's gender: female 𝝆𝟏 : 로맨스 영화를 좋아해요. (i love romance movies.) 𝝆𝟐 : 테니스를 배우고 있어요. (i am learning tennis.) 𝝆𝟑 : 남자 친구가 있어요. (i have a boy friend.) 𝝆𝟒 : 네일 아티스트로 일하고 있어요. (i work as a nail artist.) 𝝆𝟓 : 나는 김밥 좋아해요. (i like kimbap.) user persona attributes new user persona attributes 𝝆𝟔 : 떡볶이를 좋아해요. (i like tteokbokki.) 𝝆𝟕 : 미드를 즐겨요. (i enjoy watching america dramas.) (* new persona attributes are added in next sessions.) et al. (2019) addresses both what and how by generating persona-grounded responses via cvae with a selected persona from a memory. xu et al. (2022) and bae et al. ( 2022 ) also tackle the same problems via a persona retrieval module and a generator module. however, to the best of our knowledge, there has been no work on addressing all three wwh questions in pd system. therefore, considering the crucial importance of addressing the wwh issues in a commercial system, we propose novel methods to tackle all three wwh questions, which are mission-critical for a commercial system. ## dataset to develop a pd system that addresses the wwh problems, we construct a korean multi-session personalized dialogue dataset, which we refer to as mspd. this dataset includes an agent that performs several unique roles, setting it apart from other pd datasets. primarily, the agent is required to remember user persona attributes, including any persona attributes introduced during the conversation. the agent must also produce personalized responses that are both reasonably and timely grounded on the persona. the goal of this dataset is to enable a model to learn the how and when of grounding. on average, the dataset contains 4 sessions per episode, with each session consisting of 10-12 turns between the user and the agent. this format allows the agent to learn how to sustain a natural conversation flow, both within and between sessions. as illustrated in the red and blue text in figure 3 , we annotate the persona used in responses and user utterances that include personal information the model should remember. specifically, to address the when and how problems from a dataset perspective, we cap the number of personalized responses per session at two or fewer, and performed rigorous reviews to ensure the quality and appropriateness of personalized responses within conversations. this approach allows us to construct 13,469 episodes in total. statistics and other samples from the mspd dataset can be found in appendices a and b. alongside the mspd, we incorporate a variety of informal dialogue datasets, referred to as d casual , to train a more balanced model capable of generating high-quality daily, knowledgebased, empathetic, and personalized conversations. d casual consists of a comprehensive collection of approximately 12.5 million utterances. we use carefully-curated korean dialogue datasets available online foot_0 , developed by national information society agency (nia), as well as crowdsourced conversational datasets, including korean versions of personachat, empatheticdialogues, and wizard of wikipedia @xcite @xcite . ## methodology as shown in figure 2 , we train our model during the training phase to address the what and when questions using a variety of methods. these include different types of negative persona augmentation, dataset blending, and response type generation. during the inference phase, given the dialogue context and a subset of persona attributes, the model is capable of generating suitable personalized responses. these persona subsets are retrieved from individual persona attributes determined by the context of the conversation. additionally, the model provides an explanation for its decision through response type labels (rtl). conditioning on the rtl, allows us to explicitly control the generation of a personalized response. ## experiments 5.1 experimental setup to validate the efficacy of our proposed methods in building a controllable personalized dialogue (pd) system that addresses the wwh problems, we compare the performance of several models. these are enhanced with fine-tuned baseline models, such as dataset blending and negative sampling methods. additionally, by comparing models trained with different blending weights, we evaluate the impact of the blending weight on the model's grounding propensity and fluency. the baseline models are all derived from our in-house 18b parameter pretrained language model, which shares the same architecture as gpt-3 @xcite . all experiments are conducted on skt's proprietary supercomputer, titan, equipped with nvidia a100 sxm4 80gb gpus.