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Improving statistical machine translation performance by training data selection and optimization

Improving statistical machine translation performance by tra...

2007 Joint Conference on Empirical Methods in Natural Language processing and Computational Natural Language Learning, EMNLP-CoNLL 2007

作者： Lü, Yajuan Huang, Jin Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100080 China

Parallel corpus is an indispensable resource for translation model training in statistical machine translation (SMT). Instead of collecting more and more parallel training corpora, this paper aims to improve SMT performance by exploiting full potential of the existing parallel corpora. Two kinds of methods are proposed: offline data optimization and online model optimization. The offline method adapts the training data by redistributing the weight of each training sentence pairs. The online method adapts the translation model by redistributing the weight of each predefined submodels. information retrieval model is used for the weighting scheme in both methods. Experimental results show that without using any additional resource, both methods can improve SMT performance significantly. © 2007 Association for Computational Linguistics.

关键词： Machine translation

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Weighted alignment matrices for statistical machine translation

Weighted alignment matrices for statistical machine translat...

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2009 Conference on Empirical Methods in Natural Language processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009

作者： Liu, Yang Xia, Tian Xiao, Xinyan Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China

Current statistical machine translation systems usually extract rules from bilingual corpora annotated with 1-best alignments. They are prone to learn noisy rules due to alignment mistakes. We propose a new structure called weighted alignment matrix to encode all possible alignments for a parallel text compactly. The key idea is to assign a probability to each word pair to indicate how well they are aligned. We design new algorithms for extracting phrase pairs from weighted alignment matrices and estimating their probabilities. Our experiments on multiple language pairs show that using weighted matrices achieves consistent improvements over using n-best lists in significant less extraction time. © 2009 ACL and AFNLP.

关键词： Alignment

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Dynamic description logic based semantics web service composition

Dynamic description logic based semantics web service compos...

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9th International Conference on Semantics, Knowledge and Grids, SKG 2013

作者： Zhang, Jianhua Wang, Wenjie Shi, Zhongzhi Tian, Dongping Yue, Jinpeng Zhang, Bo Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781479930128

Web service composition is one of the fundamental technologies in implementing Service Oriented Architecture (SOA) based applications. Description Logic based Web service composition can easily express the static knowledge, however it is lack the dynamic knowledge support. Dynamic Description Logic extends from Description Logic and it can represent both static and dynamic knowledge. In this paper, we propose a Web service composition method base on AI planning which introduce the action dimension to the Web service composition. Finally, we give a simple example to show the algorithm's validity. © 2013 IEEE.

关键词： Computer circuits

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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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Improving Tree-to-Tree Translation with Packed Forests

Improving Tree-to-Tree Translation with Packed Forests

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Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th International Joint Conference on Natural Language processing of the AFNLP, ACL-IJCNLP 2009

作者： Liu, Yang Lü, Yajuan Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing100190 China

ISBN: (纸本)9781617382581

Current tree-to-tree models suffer from parsing errors as they usually use only 1-best parses for rule extraction and decoding. We instead propose a forest-based tree-to-tree model that uses packed forests. The model is based on a probabilistic synchronous tree substitution grammar (STSG), which can be learned from aligned forest pairs automatically. The decoder finds ways of decomposing trees in the source forest into elementary trees using the source projection of STSG while building target forest in parallel. Comparable to the state-of-the-art phrase-based system Moses, using packed forests in tree-to-tree translation results in a significant absolute improvement of 3.6 BLEU points over using 1-best trees. © 2009 ACL and AFNLP.

关键词： Decoding

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Adjoining tree-to-string translation

Adjoining tree-to-string translation

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49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011

作者： Liu, Yang Liu, Qun Lü, Yajuan Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China

ISBN: (纸本)9781932432879

We introduce synchronous tree adjoining grammars (TAG) into tree-to-string translation, which converts a source tree to a target string. Without reconstructing TAG derivations explicitly, our rule extraction algorithm directly learns tree-to-string rules from aligned Treebank-style trees. As tree-to-string translation casts decoding as a tree parsing problem rather than parsing, the decoder still runs fast when adjoining is included. Less than 2 times slower, the adjoining tree-tostring system improves translation quality by +0.7 BLEU over the baseline system only allowing for tree substitution on NIST Chinese- English test sets. © 2011 Association for Computational Linguistics.

关键词： Trees (mathematics)

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Local regression model for automatic face sketch generation

Local regression model for automatic face sketch generation

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International Conference on Image and Graphics

作者： Ji, Naye Chai, Xiujuan Shan, Shiguang Chen, Xilin Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Graduate University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9780769545417

As one of the important artistic styles of portrait, sketch portrait has wide applications for both digital entertainment and law enforcement. In this paper, an automatic face sketch generation approach is presented by learning from photo-sketch pair examples. Specifically, the relationship between a face photo and its corresponding face sketch is learned on image patch level. By applying this relationship to the input face photo patch, we can infer the output face sketch patch by exploiting some regression techniques such as kNN, the Lasso and so on. Via our local regression model, we can synthesize an appealing sketch portrait from a given face photo in a few minutes. Experiments conducted on CUHK database have shown that our results are more compelling than previous methods especially in two respects: (1) our synthesized sketches preserve more identity information of the original face photo;(2) our synthesized sketches presents more pencil sketch texture. © 2011 IEEE.

关键词： Regression analysis

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Fusion recommendation algorithm based on DeepWalk and Item-Based collaborative filtering 3

Fusion recommendation algorithm based on DeepWalk and Item-B...

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3rd International Conference on Computer Science and Management Technology, ICCSMT 2022

作者： Yu, Yicheng Li, Rongbin Yin, Jun Qinghai Normal University School of Computing Xi'Ning China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xi'Ning China

ISBN: (纸本)9798350320404

Collaborative filtering techniques have been applied to personalised recommendation systems in recommender systems. However, with the gradual increase in the number of platform users and goods, the user rating data on the entire project matrix is extremely sparse, resulting in the inaccuracy of the prediction results. DeepWalk, on the other hand, still performs well when the data is sparse. Therefore, DeepWalk is introduced into collaborative filtering, and a fusion recommendation algorithm based on DeepWalk and collaborative filtering is proposed to reduce the impact of cold start and sparsity on the recommendation results. Experiments show that the proposed fusion recommendation algorithm has higher recommendation accuracy when the data is sparse compared with other traditional algorithms, which improves the recommendation quality to a certain extent. © 2022 IEEE.

关键词： Collaborative filtering

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A secure distributed key management scheme for ad hoc network

A secure distributed key management scheme for ad hoc networ...

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2010 IEEE International Conference on information Theory and information Security, ICITIS 2010

作者： Xu, Yan Zhong, Hong Yuan, Xianping Yu, Jia Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China College of Information Engineering Qingdao University Qingdao China

ISBN: (纸本)9781424469406

An identity-based threshold key management scheme without secure channel is proposed for ad hoc network. The master private key, which is shared among all nodes by the Shamir's secret sharing scheme, is produced by all nodes when network is formed. The nodes'public keys are derived from their identities. In order to get the private key, each node needs to prove their identity to distributed CAs using a zero-knowledge proof protocol to get the share of private key. Compared with former schemes, our scheme doesn't need any local registration authority(LRA), which is easy to be compromised by adversary. When a node leaves the network, shares of the master private key would be renewed. In the end, we prove our scheme is correct and secure. © 2010 IEEE.

关键词： Ad hoc networks

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Network link weight optimization based on antisymmetric deep graph networks and reinforcement learning 6

Network link weight optimization based on antisymmetric deep...

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6th International Conference on Next Generation Data-Driven Networks, NGDN 2024

作者： Chen, Yuhan Guo, Yingya Department of Computer and Data Science Fuzhou University Fuzhou China Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9798350388374

Route optimization is a key core technology to optimize network traffic distribution, achieve network load balancing, and improve network performance. Traditional distributed networks widely run shortest-path based routing protocols, and the path of traffic is determined by the link weights of the distributed network, so routing optimization methods are usually optimized around the link weights of the network. Heuristics-based link weight optimization methods are widely used. However, heuristic methods rely on manually set rules, which are poorly generalized and cannot adapt well to dynamically changing traffic demands. Compared to heuristics, deep reinforcement learning (DRL) has the advantage of extracting more accurate feature representations in addition to its ability to handle high-dimensional state and action spaces. We propose a network link weight optimization method based on anti-symmetric deep graph networks (A-DGN) and reinforcement learning using a novel GNN framework anti-symmetric deep graph networks, where link weights are adjusted to reduce the network link utilization with the optimization objective of minimizing the maximum link utilization in the network. Experimental results show that the proposed method achieves significant performance improvements in the link weight optimization problem in four real-world network topology scenarios. © 2024 IEEE.

关键词： Deep reinforcement learning

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