基于知识的开放域对话生成旨在构建利用挖掘到的支撑知识与人类闲聊的系统。此前已有研究表明多种类型和来源的知识可用作支撑知识。即便在大语言模型时代，基于从额外最新来源检索到的知识生成回复仍是一种具有实际重要性的方法。\n现有使用单源知识的研究已证实知识选择性能与回复生成性能之间存在明确的正相关关系，但目前尚无用于评估支撑知识检索的多源数据集。此外，现有研究默认测试时可用的知识源与训练时的知识源一致，这一不符合实际的假设会不必要地限制模型能力，因为模型训练完成后可能会出现新的知识源。\n本文提出了一个名为multi-source Wizard of Wikipedia（Ms.WoW，多源维基百科向导）的高质量基准，用于评估多源对话知识选择与回复生成。与现有数据集不同，该基准包含经过清理的支撑知识，在话语层面进行锚定并划分为多个知识源。我们进一步提出了“对话知识即插即用”这一新挑战，旨在测试已训练的对话模型以零样本方式使用来自此前未见过的知识源的新支撑知识的能力。
英文摘要：
Knowledge-based, open-domain dialogue generation aims to build chit-chat systems that talk to humans using mined support knowledge. Many types and sources of knowledge have previously been shown to be useful as support knowledge. Even in the era of large language models, response generation grounded in knowledge retrieved from additional up-to-date sources remains a practically important approach. While prior work using single-source knowledge has shown a clear positive correlation between the performances of knowledge selection and response generation, there are no existing multi-source datasets for evaluating support knowledge retrieval. Further, prior work has assumed that the knowledge sources available at test time are the same as during training. This unrealistic assumption unnecessarily handicaps models, as new knowledge sources can become available after a model is trained. In this paper, we present a high-quality benchmark named multi-source Wizard of Wikipedia (Ms.WoW) for evaluating multi-source dialogue knowledge selection and response generation. Unlike existing datasets, it contains clean support knowledge, grounded at the utterance level and partitioned into multiple knowledge sources. We further propose a new challenge, dialogue knowledge plug-and-play, which aims to test an already trained dialogue model on using new support knowledge from previously unseen sources in a zero-shot fashion.
The Hong Kong University of Science and Technology（香港科技大学）；Caltech（加州理工学院）
There has been considerable progress made towards conversational models that generate coherent and fluent responses; however, this often involves training large language models on large dialogue datasets, such as Reddit. These large conversational models provide little control over the generated responses, and this control is further limited in the absence of annotated conversational datasets for attribute specific generation that can be used for fine-tuning the model. In this paper, we first propose and evaluate plug-and-play methods for controllable response generation, which does not requir…
MOOM: Maintenance, Organization and Optimization of Memory in Ultra-Long Role-Playing Dialogues
Beijing University of Posts and Telecommunications（北京邮电大学）；SenseTime（商汤科技）；Beijing Key Laboratory of Network System and Network Culture（北京网络系统与网络文化重点实验室）
JD AI Research（京东AI研究院）；University of California, Berkeley（加利福尼亚大学伯克利分校）；Renmin University of China（中国人民大学）
Existing pre-trained language models (PLMs) have demonstrated the effectiveness of self-supervised learning for a broad range of natural language processing (NLP) tasks. However, most of them are not explicitly aware of domain-specific knowledge, which is essential for downstream tasks in many domains, such as tasks in e-commerce scenarios. In this paper, we propose K-PLUG, a knowledge-injected pre-trained language model based on the encoder-decoder transformer that can be transferred to both natural language understanding and generation tasks. We verify our method in a diverse range of e-comm…
University of Cambridge（剑桥大学）；Amazon AWS AI（亚马逊AWS人工智能）；The Chinese University of Hong Kong（香港中文大学）
Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems. Despite their success, existing methods often formulate this task as a cascaded generation problem which can lead to error accumulation across different sub-tasks and greater data annotation overhead. In this study, we present PPTOD, a unified plug-and-play model for task-oriented dialogue. In addition, we introduce a new dialogue multi-task pre-training strategy that allows the model to learn the primary TOD task completion skills from heterogeneous dialog corpora. We extensively test our mod…
National Taiwan University（台湾大学）；Intel Labs（英特尔研究院）
Much literature has shown that prompt-based learning is an efficient method to make use of the large pre-trained language model. Recent works also exhibit the possibility of steering a chatbot's output by plugging in an appropriate prompt. Gradient-based methods are often used to perturb the prompts. However, some language models are not even available to the public. In this work, we first explored the combination of prompting and reinforcement learning (RL) to steer models' generation without accessing any of the models' parameters. Second, to reduce the training effort and enhance the genera…
Columbia University（哥伦比亚大学）；Microsoft Research（微软研究院）
Knowledge-grounded dialogue systems are challenging to build due to the lack of training data and heterogeneous knowledge sources. Existing systems perform poorly on unseen topics due to limited topics covered in the training data. In addition, heterogeneous knowledge sources make it challenging for systems to generalize to other tasks because knowledge sources in different knowledge representations require different knowledge encoders. To address these challenges, we present PLUG, a language model that homogenizes different knowledge sources to a unified knowledge representation for knowledge…
InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language Models
School of Artificial Intelligence and Automation, HUST（华中科技大学人工智能与自动化学院）；School of Artificial Intelligence, BUPT（北京邮电大学人工智能学院）；Sino-French Engineer School, Beihang University（北京航空航天大学中法工程师学院）；Meituan Inc.（美团公司）
Personalized Large Language Model Assistant with Evolving Conditional Memory
The Hong Kong Polytechnic University（香港理工大学）；INF Technology (Shanghai) co., Ltd.（INF科技（上海）有限公司）；Institute of Artificial Intelligence, Soochow University, China（苏州大学人工智能研究院）
Center for Artificial Intelligence Research (CAiRE)（人工智能研究中心（CAiRE））；Department of Electronic and Computer Engineering（电子与计算机工程系）；The Hong Kong University of Science and Technology（香港科技大学）
In this paper, we propose Minimalist Transfer Learning (MinTL) to simplify the system design process of task-oriented dialogue systems and alleviate the over-dependency on annotated data. MinTL is a simple yet effective transfer learning framework, which allows us to plug-and-play pre-trained seq2seq models, and jointly learn dialogue state tracking and dialogue response generation. Unlike previous approaches, which use a copy mechanism to "carryover" the old dialogue states to the new one, we introduce Levenshtein belief spans (Lev), that allows efficient dialogue state tracking with a minima…
Peking University（北京大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Tencent Cloud Xiaowei（腾讯云小微）
While interacting with chatbots, users may elicit multiple intents in a single dialogue utterance. Instead of training a dedicated multi-intent detection model, we propose DialogUSR, a dialogue utterance splitting and reformulation task that first splits multi-intent user query into several single-intent sub-queries and then recovers all the coreferred and omitted information in the sub-queries. DialogUSR can serve as a plug-in and domain-agnostic module that empowers the multi-intent detection for the deployed chatbots with minimal efforts. We collect a high-quality naturally occurring datase…
Discourse-Aware Dual-Track Streaming Response for Low-Latency Spoken Dialogue Systems
School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；Department of Language Science and Technology, Hong Kong Polytechnic University（香港理工大学语言科学与技术系）；Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology（深圳先进技术大学人工智能研究院）；School of Data Science, Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；School of Artificial Intelligence, Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）人工智能学院）
University of Edinburgh（爱丁堡大学）；McGill University（麦吉尔大学）；Mila（米拉）；Indian Institute of Technology Madras（印度理工学院马德拉斯分校）；Robert Bosch Centre for Data Science and Artificial Intelligence (RBC-DSAI), Indian Institute of Technology Madras（印度理工学院马德拉斯分校罗伯特·博世数据科学与人工智能中心（RBC-DSAI））
We consider the task of generating dialogue responses from background knowledge comprising of domain specific resources. Specifically, given a conversation around a movie, the task is to generate the next response based on background knowledge about the movie such as the plot, review, Reddit comments etc. This requires capturing structural, sequential and semantic information from the conversation context and the background resources. This is a new task and has not received much attention from the community. We propose a new architecture that uses the ability of BERT to capture deep contextual…
