Generative seq2seq dialogue systems are trained to predict the next word in dialogues that have already occurred. They can learn from large unlabeled conversation datasets, build a deep understanding of conversational context, and generate a wide variety of responses. This flexibility comes at the cost of control. Undesirable responses in the training data will be reproduced by the model at inference time, and longer generations often don't make sense. Instead of generating responses one word at a time, we train a classifier to choose from a predefined list of full responses. The classifier is trained on (conversation context, response class) pairs, where each response class is a noisily labeled group of interchangeable responses. At inference, we generate the exemplar response associated with the predicted response class. Experts can edit and improve these exemplar responses over time without retraining the classifier or invalidating old training data. Human evaluation of 775 unseen doctor/patient conversations shows that this tradeoff improves responses. Only 12% of our discriminative approach's responses are worse than the doctor's response in the same conversational context, compared to 18% for the generative model. A discriminative model trained without any manual labeling of response classes achieves equal performance to the generative model.
Deep Reinforcement Learning for On-line Dialogue State Tracking
Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Eng.（上海市教育委员会智能交互与认知工程重点实验室）；SpeechLab, Department of Computer Science and Engineering（计算机科学与工程系语音实验室）；Brain Science and Technology Research Center（脑科学与技术研究中心）；Shanghai Jiao Tong University, Shanghai, China（上海交通大学）
Planning with Diffusion Models for Target-Oriented Dialogue Systems
Department of Computer Science and Engineering, The Ohio State University, USA（计算机科学与工程系，俄亥俄州立大学）；Department of Biomedical Informatics, The Ohio State University, USA（生物医学信息学系，俄亥俄州立大学）；Translational Data Analytics Institute, The Ohio State University, USA（转化数据分析研究所，俄亥俄州立大学）
clem:todd: A Framework for the Systematic Benchmarking of LLM-Based Task-Oriented Dialogue System Realisations
Computational Linguistics, Department of Linguistics University of Potsdam（普腾多夫大学语言学系）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
An Efficient Task-Oriented Dialogue Policy: Evolutionary Reinforcement Learning Injected by Elite Individuals
School of Computer Science and Technology, Changsha University of Science and Technology, China（长沙理工大学计算机科学与技术学院）；School of Computer Science and Engineering, Central South University, China（中南大学计算机科学与工程学院）；School of Information and Computing Sciences, Utrecht University, Netherlands（乌特勒支大学信息与计算科学学院）
MORTAR: Multi-turn Metamorphic Testing for LLM-based Dialogue Systems
Faculty of Information Technology, Monash University（墨尔本大学信息科技学院）；School of Computing Technologies, RMIT University（皇家墨尔本理工大学计算技术学院）；School of Science, Computing and Emerging Technologies, Swinburne University of Technology（斯威本理工大学科学、计算与新兴技术学院）
DAM: Deliberation, Abandon and Memory Networks for Generating Detailed and Non-repetitive Responses in Visual Dialogue
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Beihang University（北京航空航天大学）；University of Chinese Academy of Sciences（中国科学院大学）；Codemao Inc.（编程猫公司）；University of Adelaide（阿德莱德大学）
