Conversational recommender system is an emerging area that has garnered an increasing interest in the community, especially with the advancements in large language models (LLMs) that enable diverse reasoning over conversational input. Despite the progress, the field has many aspects left to explore. The currently available public datasets for conversational recommendation lack specific user preferences and explanations for recommendations, hindering high-quality recommendations. To address such challenges, we present a novel conversational recommendation dataset named PEARL, synthesized with persona- and knowledge-augmented LLM simulators. We obtain detailed persona and knowledge from real-world reviews and construct a large-scale dataset with over 57k dialogues. Our experimental results demonstrate that utterances in PEARL include more specific user preferences, show expertise in the target domain, and provide recommendations more relevant to the dialogue context than those in prior datasets.
A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations
University of Southern California（南加州大学）；Adobe Research（Adobe研究院）；University of California, San Diego（加州大学圣地亚哥分校）；Intel AI Research（英特尔人工智能研究）；University of Maryland, College Park（马里兰大学学院市分校）；Vanderbilt University（范德比大学）；Virginia Polytechnic Institute and State University（弗吉尼亚理工学院和州立大学）；University of Notre Dame（诺特丹大学）；University of Oregon（俄勒冈大学）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Arizona State University（亚利桑那州立大学）
Exploring the Impact of Personality Traits on Conversational Recommender Systems: A Simulation with Large Language Models
Chinese University of Hong Kong（香港中文大学）；National University of Singapore（新加坡国立大学）；Singapore Management University（新加坡管理大学）；Hong Kong Baptist University（香港浸会大学）；Huazhong University of Science and Technology（华中科技大学）
PRISM of Opinions: A Persona-Reasoned Multimodal Framework for User-centric Conversational Stance Detection
Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学（深圳））；Peng Cheng Laboratory, Shenzhen, China（鹏城实验室）；Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies（广东省新型安全智能技术重点实验室）；The Hong Kong Polytechnic University, Hong Kong, China（香港理工大学）；Macau University of Science and Technology, Hong Kong, China（澳门科学技术大学）
Exploring the Potential of LLMs as Personalized Assistants: Dataset, Evaluation, and Analysis
Department of Electrical and Computer Engineering, Seoul National University（电子与计算机工程系，首尔国立大学）；Interdisciplinary Program in Artificial Intelligence, Seoul National University（人工智能跨学科计划，首尔国立大学）；AIIS, ASRI, INMC, and ISRC, Seoul National University（人工智能研究所、人工智能研究机构、智能网络中心和智能系统研究组，首尔国立大学）
One Size doesn't Fit All: A Personalized Conversational Tutoring Agent for Mathematics Instruction
School of Computer Science, Wuhan University（武汉大学计算机学院）；DAMO Academy, Alibaba Group（阿里巴巴达摩院）；College of Computer and Cyber Security, Hebei Normal University（河北师范大学计算机与网络空间安全学院）
针对数学教学中学习者差异被忽视的问题，本文提出 PACE 个性化对话辅导智能体，结合学习风格画像、苏格拉底式反馈与个性化训练，提升教学适应性和学习效果。
ConvoDrift: A Multi-Turn Conversational Dataset for Modeling Stylistic Tone Evolution
Informatics Institute of Technology, Sri Lanka（斯里兰卡信息技术学院）；National Health and Medical Research Council, Australia（澳大利亚国家健康与医学研究理事会）；Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg（卢森堡大学系统生物医学中心）
Towards Stable and Personalised Profiles for Lexical Alignment in Spoken Human-Agent Dialogue
Centre for Language Studies, Centre for Language and Speech Technology, Radboud University,Nijmegen, The Netherlands（语言研究所以及语言与语音技术中心，拉德堡德大学，尼姆egen，荷兰）；Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands（行为科学研究所，拉德堡德大学，尼姆egen，荷兰）
Microsoft Research Asia（微软亚洲研究院）；State Key Laboratory of Cognitive Intelligence, iFLYTEK Research（科大讯飞研究院认知智能国家重点实验室）
Personas are useful for dialogue response prediction. However, the personas used in current studies are pre-defined and hard to obtain before a conversation. To tackle this issue, we study a new task, named Speaker Persona Detection (SPD), which aims to detect speaker personas based on the plain conversational text. In this task, a best-matched persona is searched out from candidates given the conversational text. This is a many-to-many semantic matching task because both contexts and personas in SPD are composed of multiple sentences. The long-term dependency and the dynamic redundancy among …
How to Leverage Personal Textual Knowledge for Personalized Conversational Information Retrieval
Université de Montréal（蒙特利尔大学）；Dalian University of Technology（大连理工大学）；Beijing Jiaotong University（北京交通大学）；University of California, Riverside（加州大学河滨分校）
CharacterChat: Learning towards Conversational AI with Personalized Social Support
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机技术研究所）；University of Electronic Science and Technology of China（电子科技大学）
Harbin Institute of Technology（哈尔滨工业大学）；Peng Cheng Laboratory（鹏城实验室）；iFLYTEK Research（科大讯飞研究院）
In human conversations, due to their personalities in mind, people can easily carry out and maintain the conversations. Giving conversational context with persona information to a chatbot, how to exploit the information to generate diverse and sustainable conversations is still a non-trivial task. Previous work on persona-based conversational models successfully make use of predefined persona information and have shown great promise in delivering more realistic responses. And they all learn with the assumption that given a source input, there is only one target response. However, in human conv…
Conversational Intelligence requires that a person engage on informational, personal and relational levels. Advances in Natural Language Understanding have helped recent chatbots succeed at dialog on the informational level. However, current techniques still lag for conversing with humans on a personal level and fully relating to them. The University of Michigan's submission to the Alexa Prize Grand Challenge 3, Audrey, is an open-domain conversational chat-bot that aims to engage customers on these levels through interest driven conversations guided by customers' personalities and emotions. A…
User ratings play a significant role in spoken dialogue systems. Typically, such ratings tend to be averaged across all users and then utilized as feedback to improve the system or personalize its behavior. While this method can be useful to understand broad, general issues with the system and its behavior, it does not take into account differences between users that affect their ratings. In this work, we conduct a study to better understand how people rate their interactions with conversational agents. One macro-level characteristic that has been shown to correlate with how people perceive th…
Alibaba-NTU Singapore Joint Research Institute（阿里巴巴-南洋理工大学新加坡联合研究院）；Alibaba Group（阿里巴巴集团）
Empathetic conversational models have been shown to improve user satisfaction and task outcomes in numerous domains. In Psychology, persona has been shown to be highly correlated to personality, which in turn influences empathy. In addition, our empirical analysis also suggests that persona plays an important role in empathetic conversations. To this end, we propose a new task towards persona-based empathetic conversations and present the first empirical study on the impact of persona on empathetic responding. Specifically, we first present a novel large-scale multi-domain dataset for persona-…
South China University of Technology（华南理工大学）；UBTECH Robotics Corp（优必选科技股份有限公司）；Tsinghua University（清华大学）
Human language expression is based on the subjective construal of the situation instead of the objective truth conditions, which means that speakers' personalities and emotions after cognitive processing have an important influence on conversation. However, most existing datasets for conversational AI ignore human personalities and emotions, or only consider part of them. It's difficult for dialogue systems to understand speakers' personalities and emotions although large-scale pre-training language models have been widely used. In order to consider both personalities and emotions in the proce…
Spoken language understanding (SLU) systems in conversational AI agents often experience errors in the form of misrecognitions by automatic speech recognition (ASR) or semantic gaps in natural language understanding (NLU). These errors easily translate to user frustrations, particularly so in recurrent events e.g. regularly toggling an appliance, calling a frequent contact, etc. In this work, we propose a query rewriting approach by leveraging users' historically successful interactions as a form of memory. We present a neural retrieval model and a pointer-generator network with hierarchical a…
There has been an increased focus on creating conversational open-domain dialogue systems in the spoken dialogue community. Unlike traditional dialogue systems, these conversational systems cannot assume any specific information need or domain restrictions, i.e., the only inherent goal is to converse with the user on an unknown set of topics. While massive improvements in Natural Language Understanding (NLU) and the growth of available knowledge resources can partially support a robust conversation, these conversations generally lack the rapport between two humans that know each other. We deve…
Many businesses and consumers are extending the capabilities of voice-based services such as Amazon Alexa, Google Home, Microsoft Cortana, and Apple Siri to create custom voice experiences (also known as skills). As the number of these experiences increases, a key problem is the discovery of skills that can be used to address a user's request. In this paper, we focus on conversational skill discovery and present a conversational agent which engages in a dialog with users to help them find the skills that fulfill their needs. To this end, we start with a rule-based agent and improve it by using…
