Multi-turn response selection models have recently shown comparable performance to humans in several benchmark datasets. However, in the real environment, these models often have weaknesses, such as making incorrect predictions based heavily on superficial patterns without a comprehensive understanding of the context. For example, these models often give a high score to the wrong response candidate containing several keywords related to the context but using the inconsistent tense. In this study, we analyze the weaknesses of the open-domain Korean Multi-turn response selection models and publish an adversarial dataset to evaluate these weaknesses. We also suggest a strategy to build a robust model in this adversarial environment.
Data Selection for Multi-turn Dialogue Instruction Tuning
National Engineering Research Center for Software Engineering, Peking University（软件工程国家工程研究中心，北京大学）；School of Computer Science, Peking University（北京大学计算机学院）；PKU-CMCC(Hubei) Joint Research Lab for LLM Industrial Applications（北京大学-湖北CMCC大模型工业应用联合实验室）
From Myopic Selection to Long-Horizon Awareness: Sequential LLM Routing for Multi-Turn Dialogue
School of Computer Science, Shanghai Jiao Tong University, Shanghai, China（上海交通大学计算机科学学院）；WeChat, Tencent Inc, Beijing, China（腾讯北京研发中心）；University at Buffalo, New York, United States（布法罗大学）
Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots
National Engineering Laboratory for Speech and Language Information Processing, University of Science and Technology of China（中国科学技术大学语音及语言信息处理国家工程实验室）；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）
Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots
State Key Lab of Software Development Environment, Beihang University（北京航空航天大学软件开发环境国家重点实验室）；Microsoft Research（微软研究院）；College of Computer and Control Engineering, Nankai University（南开大学计算机与控制工程学院）
Dept. Computing Science & Alberta Machine Intelligence Institute (Amii), University of Alberta（计算科学系及阿尔伯塔人工智能研究所（Amii），阿尔伯塔大学）；Canada CIFAR AI Chair, Amii（加拿大CIFAR人工智能主席，Amii）
Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning
Hong Kong Polytechnic University（香港理工大学）；Baidu Inc.（百度公司）；Tencent Jarvis Lab（腾讯天衍实验室）；Université de Montréal（蒙特利尔大学）；Mila（米拉人工智能研究院）；Sun Yat-sen University（中山大学）
IRRGN: An Implicit Relational Reasoning Graph Network for Multi-turn Response Selection
College of Computer and Information Science, Southwest University（西南大学计算机与信息科学学院）；yz-intelligence Inc（元智智能公司）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots
Wangxuan Institute of Computer Technology（王选计算机技术研究所）；Center for Data Science（数据科学中心）；Academy for Advanced Interdisciplinary Studies（前沿交叉学科研究院）；Peking University（北京大学）；Soochow University（苏州大学）；Gaoling School of Artificial Intelligence（高瓴人工智能学院）；Renmin University of China（中国人民大学）
Modeling Multiple Support Strategies within a Single Turn for Emotional Support Conversations
School of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）；Qwen DianJin Team, Alibaba Cloud Computing（阿里云Qwen团队）；Xiamen University（厦门大学）
An Effective Domain Adaptive Post-Training Method for BERT in Response Selection
Korea University（高丽大学）
我们专注于多轮响应选择检索为基础的对话系统。在本文中，我们利用强大的预训练语言模型双向编码器表示从Transformer（BERT）的多轮对话系统，并提出了一个高效的特定领域的语料后训练方法。虽然BERT很容易被各种NLP任务采用，并且优于每个任务的先前基线，但如果任务语料库过于集中于某个领域，它仍然存在局限性。在特定领域语料库上进行后训练（例如，Ubuntu语料库）帮助模型训练不出现在一般语料库中的上下文化表示和单词（例如，English Wikipedia）.实验结果表明，我们的方法实现了新的国家的最先进的两个响应选择基准（即，Ubuntu Corpus V1，Advising Corpus）性能提升5.9%，R@1上提升6%。
