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A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving

Neural computing and Applications 2025年第3期37卷 1651-1672页

作者： Xu, Junjie Chen, Yuzhong Xiao, Lingsheng Liao, Hongmiao Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Math word problem (MWP) represents a critical research area within reading comprehension, where accurate comprehension of math problem text is crucial for generating math expressions. However, current approaches still grapple with unresolved challenges in grasping the sensitivity of math problem text and delineating distinct roles across various clause types, and enhancing numerical representation. To address these challenges, this paper proposes a Numerical Magnitude Aware Multi-Channel Hierarchical Encoding Network (NMA-MHEA) for math expression generation. Firstly, NMA-MHEA implements a multi-channel hierarchical context encoding module to learn context representations at three different channels: intra-clause channel, inter-clause channel, and context-question interaction channel. NMA-MHEA constructs hierarchical constituent-dependency graphs for different levels of sentences and employs a Hierarchical Graph Attention Neural Network (HGAT) to learn syntactic and semantic information within these graphs at the intra-clause and inter-clause channels. NMA-MHEA then refines context clauses using question information at the context-question interaction channel. Secondly, NMA-MHEA designs a number encoding module to enhance the relative magnitude information among numerical values and type information of numerical values. Experimental results on two public benchmark datasets demonstrate that NMA-MHEA outperforms other state-of-the-art models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantics

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Brain-inspired artificial intelligence research: A review

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Science China(Technological Sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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Scalable Constrained Policy Optimization for Safe Multi-agent Reinforcement Learning 38

Scalable Constrained Policy Optimization for Safe Multi-agen...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Zhang, Lijun Li, Lin Wei, Wei Song, Huizhong Yang, Yaodong Liang, Jiye Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Shanxi Taiyuan China Institute for AI Peking University Beijing China

A challenging problem in seeking to bring multi-agent reinforcement learning (MARL) techniques into real-world applications, such as autonomous driving and drone swarms, is how to control multiple agents safely and cooperatively to accomplish tasks. Most existing safe MARL methods learn the centralized value function by introducing a global state to guide safety cooperation. However, the global coupling arising from safety constraints and the exponential growth of the state-action space size limit their applicability in instant communication or computing resource-constrained systems and larger multi-agent systems. In this paper, we develop a novel scalable and theoretically-justified multi-agent constrained policy optimization method. This method integrates the rigorous bounds of the trust region method and the bounds of the truncated advantage function to provide a new local policy optimization objective for each agent. Also, we prove that the safety constraints and the joint policy improvement can be met when each agent adopts a sequential update scheme to optimize a κ-hop policy. Furthermore, we propose a practical algorithm called Scalable MAPPO-Lagrangian (Scal-MAPPO-L). The proposed method's effectiveness is verified on a collection of benchmark tasks, and the results support our theory that decentralized training with local interactions can still improve reward performance and satisfy safe constraints. © 2024 Neural information processing systems foundation. All rights reserved.

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Accurate Interpolation for Scattered Data through Hierarchical Residual Refinement 37

Accurate Interpolation for Scattered Data through Hierarchic...

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37th Conference on Neural information processing Systems, NeurIPS 2023

作者： Ding, Shizhe Xia, Boyang Bu, Dongbo Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Central China Institute for Artificial Intelligence Technologies Zhengzhou China

ISBN: (纸本)9781713899921

Accurate interpolation algorithms are highly desired in various theoretical and engineering scenarios. Unlike the traditional numerical algorithms that have exact zero-residual constraints on observed points, the neural network-based interpolation methods exhibit non-zero residuals at these points. These residuals, which provide observations of an underlying residual function, can guide predicting interpolation functions, but have not been exploited by the existing approaches. To fill this gap, we propose Hierarchical INTerpolation Network (HINT), which utilizes the residuals on observed points to guide target function estimation in a hierarchical fashion. HINT consists of several sequentially arranged lightweight interpolation blocks. The first interpolation block estimates the main component of the target function, while subsequent blocks predict the residual components using observed points residuals of the preceding blocks. The main component and residual components are accumulated to form the final interpolation results. Furthermore, under the assumption that finer residual prediction requires a more focused attention range on observed points, we utilize hierarchical local constraints in correlation modeling between observed and target points. Extensive experiments demonstrate that HINT outperforms existing interpolation algorithms significantly in terms of interpolation accuracy across a wide variety of datasets, which underscores its potential for practical scenarios. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Interpolation

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Polynomial stacked-attention network for nationality classification

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Frontiers of Computer Science 2022年第4期16卷 215-217页

作者： Kunyan Li Jie Zhang Shiguang Shan Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing100190China University of Chinese Academy of Sciences Beijing100049China

1Introduction In recent years,nationality and region inference has atracted more and_more *** anthropological researches indicate that human faces are influenced by genes,and other external factors,e.g-,geographical environment[1].This relevance provides possible solutions for geographical area inference by ***,it remains a challenging task due to the tiny differences on faces for various countries,which is even hard for human beings to tell.

关键词： Polynomial s. network

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Overview of CCL24-Eval Task 8: Evaluation of Commonsense Reasoning and Moral Understanding in Children’s Stories 23

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23rd chinese National Conference on Computational Linguistics, CCL 2024

作者： Yan, Guohang Liang, Feihao Guo, Yaxin Tan, Hongye Li, Ru Zhang, Hu School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education Shanxi University Shanxi Taiyuan030006 China

ISBN: (纸本)9780000000002

This paper provides a comprehensive review of the the CCL24-Eval Task 8: Commonsense Reasoning and Moral Understanding in Children’s Stories(CRMUS). This task has designed two sub-tasks, which aim to assess the commonsense reasoning and implicit meaning comprehension capabilities of Large Language Models(LLMs). We heve received registration forms from 33 teams, 15 of which submitted final results that exceeded the baseline score. We present the results of the top 5 teams and our analysis of these results. © 2024 China National Conference on Computational Linguistics.

关键词： Computational linguistics

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Hybrid particle swarm optimization with adaptive learning strategy

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Soft computing 2024年第17-18期28卷 9759-9784页

作者： Wang, Lanyu Tian, Dongping Gou, Xiaorui Shi, Zhongzhi School of Mathematics and Information Sciences Baoji University of Arts and Science Shaanxi Baoji721007 China Institute of Computer Software Baoji University of Arts and Sciences Shaanxi Baoji721007 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Particle swarm optimization (PSO) is a meta-heuristic algorithm that can be used to solve different real-world optimization problems. However, PSO suffers from some intrinsic drawbacks, including falling into local optimal and slow convergence in the latter evolution process. To deal with these issues, a hybrid PSO with adaptive learning strategy (abbreviated as HPSO-ALS) is proposed in this paper. To start with, the chaotic opposition-learning method is employed to generate the uniformly distributed particles so as to enhance the quality of the initial swarm. Next, a self-logical mapping-based nonlinear decreasing inertia weight is formulated to trade off the global search and local search abilities. Meanwhile, different adaptive position updating strategies are leveraged to achieve better convergence according to different evolution status. In addition, inspired by the cask effect, the global worst particle (gworst) is introduced into the velocity updating to make full use of the potential information of the particles. Finally, extensive experiments on sixteen benchmark functions and CEC’13 test suite have verified the promising performance of HPSO-ALS proposed in this paper. Alternatively, HPSO-ALS is also applied to the classical traveling salesman problem (TSP) and the experimental results have further validated the effectiveness and efficiency of our proposal. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Traveling salesman problem

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DASC-SPT: Towards Self-Supervised Panoramic Semantic Segmentation

DASC-SPT: Towards Self-Supervised Panoramic Semantic Segment...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Tan, Tianlong Chen, Bin Cao, Hongliang Yan, Chenggang Ma, Yike Dai, Feng Institute of Computing Technology Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Beijing China Shandong University China University of Chinese Academy of Sciences Beijing China Hangzhou Dianzi University China

ISBN: (纸本)9798331510831

Self-Supervised Semantic Segmentation, aiming to leverage masses of unlabeled data for boosting semantic segmentation, has been rapidly emerging as an active task in recent years. However, existing self-supervised semantic segmentation approaches mainly focus on planar images, leaving multiple distorted objects encountered in panoramic images unexplored due to the formidable challenge of handling heterogeneous degrees of distortions across different locations. In this paper, we propose a novel Self-Supervised Panoramic Semantic Segmentation model, termed DASC-SPT, built upon the mainstream contrastive learning framework. Towards distortions in panoramic images, we present two structures to better learn from distorted features by applying planar images. For the input images of self-supervision, we design a Spherical Projection Transformation (SPT) strategy that involves randomly projecting planar images onto various locations of the sphere to introduce the distortions. For pixel-wise distorted features, we construct a Deformation-aware Sampling Consistency (DASC) framework to further utilize the shared content and discrepancies caused by different distortions of paired views, where the deformation-aware consistency can be quantified on pixel-wise features. Both of the two components facilitate the model to adapt to distortions and boost panoramic semantic segmentation. Extensive comprehensive experiments on three panoramic datasets demonstrate the effectiveness and superiority of DASC-SPT approach. © 2025 IEEE.

关键词： Semantic Segmentation

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Leveraging Knowledge Distillation for Improved Event Extraction in QA Models 6

Leveraging Knowledge Distillation for Improved Event Extract...

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6th International Conference on Frontier Technologies of information and Computer, ICFTIC 2024

作者： Zhou, Peiyao Hu, Zhikui Zi, Kangli Zhang, Dawei School of Computer Science Jiangsu University of Science and Technology Zhenjiang China Institute of Computing Technology Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Beijing China

ISBN: (纸本)9798331541750

Event extraction is an important task in natural language processing, and it is widely utilized in intelligence domains such as business and military for information extraction. Recently, many works have successfully transformed document-level event extraction into Question-answering (QA) tasks with remarkable results. This approach embeds event argument information within the questions, introducing prior knowledge into the process. Jin et al. highlighted the importance of high-quality QA pairs for practical QA tasks, emphasizing that constructing these pairs remains a key challenge[1]. In this study, we propose a Prompt-based question generation method to automatically generate questions containing event arguments, which converts event extraction into a QA task. We introduce an event ontology-based information retrieval module to enhance answer accuracy and select the most relevant document segments as input text. Additionally, we employ knowledge distillation to build the QA model, transferring knowledge from a large pre-trained model to a more compact and efficient one, improving the student model's performance. Experiments show that our model performs strongly in mainstream benchmarks, with 4.8 improvements on WikiEvents. © 2024 IEEE.

关键词： Question answering

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Monotonicity and nonmonotonicity in L3-valued propositional logic

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Frontiers of Computer Science 2022年第4期16卷 33-43页

作者： Wei Li Yuefei Sui State Key Laboratory of Software Development Environment Beihang UniversityBeijing100083China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing100049China

A sequent is a pair (Γ, Δ), which is true under an assignment if either some formula in Γ is false, or some formula in Δ is true. In L_(3)-valued propositional logic, a multisequent is a triple Δ∣Θ∣Γ, which is true under an assignment if either some formula in Δ has truth-value t, or some formula in Θ has truth-value m, or some formula in Γ has truth-value f. There is a sound, complete and monotonic Gentzen deduction system G for sequents. Dually, there is a sound, complete and nonmonotonic Gentzen deduction system G′ for co-sequents Δ: Θ: Γ. By taking different quantifiers some or every, there are 8 kinds of definitions of validity of multisequent Δ∣Θ∣Γ and 8 kinds of definitions of validity of co-multisequent Δ: Θ: Γ, and correspondingly there are 8 sound and complete Gentzen deduction systems for sequents and 8 sound and complete Gentzen deduction systems for co-sequents. Correspondingly their monotonicity is discussed.

关键词： sequent mulisequent gentzen deduction system monotonicity nonmonotonicity

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