Natural language understanding (NLU) is a core component of a spoken dialogue system. Recently recurrent neural networks (RNN) obtained strong results on NLU due to their superior ability of preserving sequential information over time. Traditionally, the NLU module tags semantic slots for utterances considering their flat structures, as the underlying RNN structure is a linear chain. However, natural language exhibits linguistic properties that provide rich, structured information for better understanding. This paper introduces a novel model, knowledge-guided structural attention networks (K-SAN), a generalization of RNN to additionally incorporate non-flat network topologies guided by prior knowledge. There are two characteristics: 1) important substructures can be captured from small training data, allowing the model to generalize to previously unseen test data; 2) the model automatically figures out the salient substructures that are essential to predict the semantic tags of the given sentences, so that the understanding performance can be improved. The experiments on the benchmark Air Travel Information System (ATIS) data show that the proposed K-SAN architecture can effectively extract salient knowledge from substructures with an attention mechanism, and outperform the performance of the state-of-the-art neural network based frameworks.
Teacher-Guided Policy Optimization for On-Policy Reasoning Distillation under Large Policy Divergence
School of Computer Science and Engineering, Northeastern University, China（东北大学计算机科学与工程学院）；Tsinghua University（清华大学）；Meituan（美团）；Meta AI；NiuTrans Research, Shenyang, China（新译研究院，沈阳，中国）
Teacher-Student Gaps Are Not Enough: Outcome-Guided On-Policy Distillation for Multi-Turn Autonomous Agents
Qwen Large Model Application Team, Alibaba（阿里巴巴通义千问大模型应用团队）；Peking University（北京大学）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Jiao Tong University（上海交通大学）
Teacher Encoder-Student Decoder Denoising Guided Segmentation Network for Anomaly Detection
School of Computer Science, Chengdu University of Information Science and Technology（成都信息科技大学计算机科学学院）；Purdue University（普渡大学）；University at Albany（阿尔巴尼大学）
Structural Knowledge Distillation: Tractably Distilling Information for Structured Predictor
ShanghaiTech University（上海科技大学）；Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences（中国科学院上海微系统与信息技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）
Teacher-Guided One-Shot Pruning via Context-Aware Knowledge Distillation
Apurba-NSU R&D Lab, Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh（阿普尔巴-NSU研发实验室，电气与计算机工程系，北方南大学，达卡，孟加拉国）；Apurba Technologies, Sunnyvale, CA 94085, USA（阿普尔巴技术公司，硅谷，加利福尼亚州94085，美国）
Mutual Information Guided Backdoor Mitigation for Pre-trained Encoders
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）
OGER: A Robust Offline-Guided Exploration Reward for Hybrid Reinforcement Learning
Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学深圳研究院）；Hithink RoyalFlush Information Network, Hangzhou, China（杭州Hithink RoyalFlush信息网络）；Computer and Information Science, University of Macau, Macau, China（澳门大学计算机与信息科学学院）
From Atoms to Chains: Divergence-Guided Reasoning Curriculum for Unlabeled LLM Domain Adaptation
Beijing Key Laboratory of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology（北京智能信息科技重点实验室，计算机科学与技术学院，北京理工大学）；ByteDance China（字节跳动中国）
On-Policy Distillation with Curriculum Turn-level Guidance for Multi-turn Agents
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所基础模型研究中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Large Language Model Department, Tencent（腾讯大语言模型部）；University of Science and Technology of China（中国科学技术大学）；Zhejiang University（浙江大学）；National University of Singapore（新加坡国立大学）；Wuhan AI Research（武汉人工智能研究院）
Learning More by Training Less: Uncertainty-Guided Progressive Contraction for Efficient Reasoning Post-Training
National University of Singapore（新加坡国立大学）；Nanjing University（南京大学）；Fudan University（复旦大学）；The University of Sydney（悉尼大学）；The Chinese University of Hong Kong（香港中文大学）；Nanyang Technological University（南洋理工大学）
CritiqueDriveVLM: From Verifier-Guided Reinforcement Learning to Latent Thought Distillation for Autonomous Driving
State Key Laboratory of Networking and Switching Technology（网络与交换技术国家重点实验室）；School of Digital Media & Design Arts（数字媒体与设计艺术学院）；Beijing University of Posts and Telecommunications（北京邮电大学）
When Smaller Wins: Dual-Stage Distillation and Pareto-Guided Compression of Liquid Neural Networks for Edge Battery Prognostics
Singapore Institute of Technology（新加坡科技学院）；Institute of Materials Research and Engineering（材料研究与工程研究所）；Agency for Science, Technology and Research（科技研究局）；Institute of High Performance Computing（高性能计算研究所）
