Machine comprehension(MC) style question answering is a representative problem in natural language processing. Previous methods rarely spend time on the improvement of encoding layer, especially the embedding of syntactic information and name entity of the words, which are very crucial to the quality of encoding. Moreover, existing attention methods represent each query word as a vector or use a single vector to represent the whole query sentence, neither of them can handle the proper weight of the key words in query sentence. In this paper, we introduce a novel neural network architecture called Multi-layer Embedding with Memory Network(MEMEN) for machine reading task. In the encoding layer, we employ classic skip-gram model to the syntactic and semantic information of the words to train a new kind of embedding layer. We also propose a memory network of full-orientation matching of the query and passage to catch more pivotal information. Experiments show that our model has competitive results both from the perspectives of precision and efficiency in Stanford Question Answering Dataset(SQuAD) among all published results and achieves the state-of-the-art results on TriviaQA dataset.
Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation
Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Hebei University of Science and Technology（河北科技大学）
Measuring Meaning Composition in the Human Brain with Composition Scores from Large Language Models
National Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；Department of Linguistics and Translation, City University of Hong Kong（香港城市大学语言及翻译学系）
MaP: A Matrix-based Prediction Approach to Improve Span Extraction in Machine Reading Comprehension
School of Information Science and Technology, Southwest Jiaotong University（西南交通大学信息科学与技术学院）；Microsoft Cognitive Services Research Group（微软认知服务研究组）；Microsoft STCA NLP Group（微软STCA自然语言处理组）
"Sorry, Come Again?" Prompting -- Enhancing Comprehension and Diminishing Hallucination with [PAUSE]-injected Optimal Paraphrasing
AI Institute, University of South Carolina（南卡罗来纳大学AI研究院）；Indian Institute of Technology, Kharagpur（印度理工学院克勒格布尔分校）；Islamic University of Technology（伊斯兰科技大学）；Stanford University（斯坦福大学）；Amazon AI（亚马逊AI研究院）
Memory Reward Inflation in Self-Improving LLM Agents
University Of North Texas（北得克萨斯大学）；Edge Hill University（边山大学）；University of Cincinnati（辛辛那提大学）；Iran University of Science and Technology（伊朗科技大学）；Islamic Azad University（伊斯兰阿扎德大学）
HGMEM: Hypergraph-based Working Memory to Improve Multi-step RAG for Long-Context Complex Relational Modeling
The Chinese University of Hong Kong.（香港中文大学）；Pengcheng Laboratory.（鹏城实验室）；WeChat AI, Tencent（微信AI，腾讯）；University of Chinese Academy of Sciences.（中国科学院大学）
CMMR-VLN: Vision-and-Language Navigation via Continual Multimodal Memory Retrieval
Foshan Graduate School of Innovation at Northeastern University（东北大学创新研究生院）；Faculty of Robot Science and Engineering at Northeastern University（东北大学机器人科学与工程学院）；College of Software at Northeastern University（东北大学软件学院）；School of Aeronautic Science and Engineering at Beihang University（北航航空科学与工程学院）
A Question Answering Approach to Emotion Cause Extraction
Shenzhen Graduate School, Harbin Institute of Technology（哈尔滨工业大学深圳研究生院）；College of Mathematics and Computer Science, Fuzhou University（福州大学数学与计算机科学学院）；School of Engineering and Applied Science, Aston University（阿斯顿大学工程与应用科学学院）；Guangdong Provincial Engineering Technology Research Center for Data Science（广东省数据科学工程技术研究中心）；Department of Computing, the Hong Kong Polytechnic University（香港理工大学计算学系）
Venus: An Efficient Edge Memory-and-Retrieval System for VLM-based Online Video Understanding
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China（计算机科学与工程学院，中山大学，广州，中国）；Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China（信息工程系，香港中文大学，香港特别行政区，中国）；Shenzhen Smart City Communications Co., Ltd., China（深圳智慧城市通信有限公司，中国）
LSTM-MAS: A Long Short-Term Memory Inspired Multi-Agent System for Long-Context Understanding
Fudan University（复旦大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Innovation Institute（上海创新研究院）；The Chinese University of Hong Kong（香港中文大学）
