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）
The task of response selection in multi-turn dialogue is to find the best option from all candidates. In order to improve the reasoning ability of the model, previous studies pay more attention to using explicit algorithms to model the dependencies between utterances, which are deterministic, limited and inflexible. In addition, few studies consider differences between the options before and after reasoning. In this paper, we propose an Implicit Relational Reasoning Graph Network to address these issues, which consists of the Utterance Relational Reasoner (URR) and the Option Dual Comparator (ODC). URR aims to implicitly extract dependencies between utterances, as well as utterances and options, and make reasoning with relational graph convolutional networks. ODC focuses on perceiving the difference between the options through dual comparison, which can eliminate the interference of the noise options. Experimental results on two multi-turn dialogue reasoning benchmark datasets MuTual and MuTual+ show that our method significantly improves the baseline of four pretrained language models and achieves state-of-the-art performance. The model surpasses human performance for the first time on the MuTual dataset.
SocraticKG: Knowledge Graph Construction via QA-Driven Fact Extraction
Department of Biosystems Engineering, Seoul National University（首尔国立大学生物系统工程系）；Artificial Intelligence Institute, Seoul National University（首尔国立大学人工智能研究所）；Interdisciplinary Program in Artificial Intelligence, Seoul National University（首尔国立大学人工智能跨学科项目）；Interdisciplinary Program in Cognitive Science, Seoul National University（首尔国立大学认知科学跨学科项目）
A word association network methodology for evaluating implicit biases in LLMs compared to humans
University of Pisa, Department of Computer Science（比萨大学计算机科学系）；National Research Council of Italy, Institute of Information Science and Technologies（意大利国家研究委员会信息科学与技术研究所）；University of Trento, Department of Psychology and Cognitive Science（特伦托大学心理学与认知科学系）
Relational Prompt-based Pre-trained Language Models for Social Event Detection
Kunming University of Science and Technology（昆明理工大学）；Beijing Institute of Technology（北京理工大学）；Beihang University（北京航空航天大学）；North China Electric Power University（华北电力大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）
Shanghai University（上海大学）；Beijing University of Technology（北京工业大学）；Engineering Research Center of Intelligence Perception and Autonomous Control, Ministry of Education（教育部智能感知与自主控制工程研究中心）
Relational Temporal Graph Reasoning for Dual-task Dialogue Language Understanding
Australian Artificial Intelligence Institute (AAII)（澳大利亚人工智能研究所）；University of Technology Sydney（悉尼科技大学）；Centre for Frontier AI Research, A*STAR（新加坡科技研究局前沿人工智能研究中心）
Relational graph-driven differential denoising and diffusion attention fusion for multimodal conversation emotion recognition
College of Computer and Mathematics, Central South University of Forestry and Technology（中南林业科技大学计算机与数学学院）；Department of Computer Science, State University of New York, New Paltz（纽约州立大学新帕尔茨分校计算机科学系）
