For natural language understanding tasks, either machine reading comprehension or natural language inference, both semantics-aware and inference are favorable features of the concerned modeling for better understanding performance. Thus we propose a Semantics-Aware Inferential Network (SAIN) to meet such a motivation. Taking explicit contextualized semantics as a complementary input, the inferential module of SAIN enables a series of reasoning steps over semantic clues through an attention mechanism. By stringing these steps, the inferential network effectively learns to perform iterative reasoning which incorporates both explicit semantics and contextualized representations. In terms of well pre-trained language models as front-end encoder, our model achieves significant improvement on 11 tasks including machine reading comprehension and natural language inference.
Do Large Language Models Advocate for Inferentialism?
College of Media Arts, Science and Technology, School of Informatics, University of Tsukuba（筑波大学媒体艺术、科学与技术学院，信息学院）；Institute of Systems and Information Engineering, University of Tsukuba（筑波大学系统与信息工程研究所）
Department of Computer Science and Engineering, Shanghai Jiao Tong University（上海交通大学计算机科学与工程系）；MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院人工智能教育部重点实验室）；College of Zhiyuan, Shanghai Jiao Tong University（上海交通大学致远学院）
Beyond Semantic Accuracy: Consequence-Aware Evaluation for Safety-Critical Language Understanding
ATMRI, Nanyang Technological University (NTU)（南洋理工大学ATMRI）；Centre of AI Research, VinUniversity（文大学人工智能研究中心）；School of Management, Indian Institute of Technology Mandi（印度理工大学曼迪分校管理学院）
Multi-Granularity Semantic Aware Graph Model for Reducing Position Bias in Emotion-Cause Pair Extraction
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）
