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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Zhang, Hainan Lan, Yanyan Pang, Liang Chen, Hongshen Ding, Zhuoye Yin, Dawei CAS Key Lab of Network Data Science and Technology Institute of Computing Technology CAS China *** Beijing China University of Chinese Academy of Sciences China

ISBN: (纸本)9780999241165

Topic drift is a common phenomenon in multi-turn dialogue. Therefore, an ideal dialogue generation models should be able to capture the topic information of each context, detect the relevant context, and produce appropriate responses accordingly. However, existing models usually use word or sentence level similarities to detect the relevant contexts, which fail to well capture the topical level relevance. In this paper, we propose a new model, named STAR-BTM, to tackle this problem. Firstly, the Biterm Topic Model is pre-trained on the whole training dataset. Then, the topic level attention weights are computed based on the topic representation of each context. Finally, the attention weights and the topic distribution are utilized in the decoding process to generate the corresponding responses. Experimental results on both Chinese customer services data and English Ubuntu dialogue data show that STAR-BTM significantly outperforms several state-of-the-art methods, in terms of both metric-based and human evaluations. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Stars

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Neural or statistical: An empirical study on language models for Chinese input recommendation on mobile 23rd

Neural or statistical: An empirical study on language models...

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23rd China conference on Information Retrieval, CCIR 2017

作者： Zhang, Hainan Lan, Yanyan Guo, Jiafeng Xu, Jun Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783319686981

Chinese input recommendation plays an important role in alleviating human cost in typing Chinese words, especially in the scenario of mobile applications. The fundamental problem is to predict the conditional probability of the next word given the sequence of previous words. Therefore, statistical language models, i.e.n-grams based models, have been extensively used on this task in real application. However, the characteristics of extremely different typing behaviors usually lead to serious sparsity problem, even n-gram with smoothing will fail. A reasonable approach to tackle this problem is to use the recently proposed neural models, such as probabilistic neural language model, recurrent neural network and word2vec. They can leverage more semantically similar words for estimating the probability. However, there is no conclusion on which approach of the two will work better in real application. In this paper, we conduct an extensive empirical study to show the differences between statistical and neural language models. The experimental results show that the two different approach have individual advantages, and a hybrid approach will bring a significant improvement. © Springer International Publishing AG 2017.

关键词： Recurrent neural networks

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Beyond Precision: A Study on Recall of Initial Retrieval with Neural Representations 1

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28th China Conference on Information Retrieval, CCIR 2022

作者： Xiao, Yan Fan, Yixing Zhang, Ruqing Guo, Jiafeng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

ISBN: (数字)9783031247552

ISBN: (纸本)9783031247545

Vocabulary mismatch is a central problem in information retrieval (IR), i.e., the relevant documents may not contain the same (symbolic) terms of the query. Recently, neural representations have shown great success in capturing semantic relatedness, leading to new possibilities to alleviate the vocabulary mismatch problem in IR. However, most existing efforts in this direction have been devoted to the re-ranking stage. That is to leverage neural representations to help re-rank a set of candidate documents, which are typically obtained from an initial retrieval stage based on some symbolic index and search scheme (e.g., BM25 over the inverted index). This naturally raises a question: if the relevant documents have not been found in the initial retrieval stage due to vocabulary mismatch, there would be no chance to re-rank them to the top positions later. Therefore, in this paper, we study the problem how to employ neural representations to improve the recall of relevant documents in the initial retrieval stage. Specifically, to meet the efficiency requirement of the initial stage, we introduce a neural index for the neural representations of documents, and propose two hybrid search schemes based on both neural and symbolic indices, namely the parallel search scheme and the sequential search scheme. Our experiments show that both hybrid index and search schemes can improve the recall of the initial retrieval stage with small overhead. © 2023, Springer Nature Switzerland AG.

关键词： Semantics

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Understanding and Improving Neural Ranking Models from a Term Dependence View 15th

Understanding and Improving Neural Ranking Models from a Ter...

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15th Asia Information Retrieval Societies Conference, AIRS 2019

作者： Fan, Yixing Guo, Jiafeng Lan, Yanyan Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783030428341

Recently, neural information retrieval (NeuIR) has attracted a lot of interests, where a variety of neural models have been proposed for the core ranking problem. Beyond the continuous refresh of the state-of-the-art neural ranking performance, the community calls for more analysis and understanding of the emerging neural ranking models. In this paper, we attempt to analyze these new models from a traditional view, namely term dependence. Without loss of generality, most existing neural ranking models could be categorized into three categories with respect to their underlying assumption on query term dependence, i.e., independent models, dependent models, and hybrid models. We conduct rigorous empirical experiments over several representative models from these three categories on a benchmark dataset and a large click-through dataset. Interestingly, we find that no single type of model can achieve a consistent win over others on different search queries. An oracle model which can select the right model for each query can obtain significant performance improvement. Based on the analysis we introduce an adaptive strategy for neural ranking models. We hypothesize that the term dependence in a query could be measured through the divergence between its independent and dependent representations. We thus propose a dependence gate based on such divergence representation to softly select neural ranking models for each query accordingly. Experimental results verify the effectiveness of the adaptive strategy. © Springer Nature Switzerland AG 2020.

关键词： Large dataset

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Academic access data analysis for literature recommendation 23rd

Academic access data analysis for literature recommendation

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23rd China conference on Information Retrieval, CCIR 2017

作者： Fan, Yixing Guo, Jiafeng Lan, Yanyan Xu, Jun Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783319686981

Academic reading plays an important role in researchers’ daily life. To alleviate the burden of seeking relevant literature from rapidly growing academic repository, different kinds of recommender systems have been introduced in recent years. However, most existing work focused on adopting traditional recommendation techniques, like content-based filtering or collaborative filtering, in the literature recommendation scenario. Little work has yet been done on analyzing the academic reading behaviors to understand the reading patterns and information needs of real-world academic users, which would be a foundation for improving existing recommender systems or designing new ones. In this paper, we aim to tackle this problem by carrying out empirical analysis over large scale academic access data, which can be viewed as a proxy of academic reading behaviors. We conduct global, group-based and sequence-based analysis to address the following questions: (1) Are there any regularities in users’ academic reading behaviors? (2) Will users with different levels of activeness exhibit different information needs? (3) How to correlate one’s future demands with his/her historical behaviors? By answering these questions, we not only unveil useful patterns and strategies for literature recommendation, but also identify some challenging problems for future development. © Springer International Publishing AG 2017.

关键词： Recommender systems

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AdaptFRCNet: Semi-supervised adaptation of pre-trained model with frequency and region consistency for medical image segmentation

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Medical Image Analysis 2025年 103卷

作者： Along He Yanlin Wu Zhihong Wang Tao Li Huazhu Fu College of Computer Science Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin 300350 China Haihe Lab of ITAI Tianjin 300459 China Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) 138632 Singapore

Recently, large pre-trained models (LPM) have achieved great success, which provides rich feature representation for downstream tasks. Pre-training and then fine-tuning is an effective way to utilize LPM. However, the application of LPM in the medical domain is hindered by the presence of a large number of parameters and a limited amount of labeled data. In clinical practice, there exists a substantial amount of unlabeled data that remains underutilized. Semi-supervised learning emerges as a promising approach to harnessing these unlabeled data effectively. In this paper, we propose semi-supervised adaptation of pre-trained model with frequency and region consistency (AdaptFRCNet) for medical image segmentation. Specifically, the pre-trained model is frozen and the proposed lightweight attention-based adapters (Att_Adapter) are inserted into the frozen backbone for parameter-efficient fine-tuning (PEFT). We propose two consistency regularization strategies for semi-supervised learning: frequency domain consistency (FDC) and multi-granularity region similarity consistency (MRSC). FDC aids in learning features within the frequency domain, and MRSC aims to achieve multiple region-level feature consistencies, capturing local context information effectively. By leveraging the proposed Att_Adapter along with FDC and MRSC, we can effectively and efficiently harness the powerful feature representation capability of the LPM. We conduct extensive experiments on three medical image segmentation datasets, demonstrating significant performance improvements over other state-of-the-art methods. The code is available at https://***/NKUhealong/AdaptFRCNet .

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Study of the shear-rate dependence of granular friction based on community detection

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science China(Physics,Mechanics & Astronomy) 2019年第4期62卷 38-44页

作者： YongWen Zhang GaoKe Hu XiaoSong Chen Wei Chen WenQi Liu Data Science Research Center Faculty of Science Kunming University of Science and Technology CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences School of Physical Sciences University of Chinese Academy of Sciences State Key Laboratory of Multiphase Complex System Institute of Process Engineering Chinese Academy of Sciences

In this study, computer simulations are performed on three-dimensional granular systems under shear conditions. The system comprises granular particles that are confined between two rigid plates. The top plate is subjected to a normal force and driven by a shearing velocity. A positive shear-rate dependence of granular friction, known as velocity-strengthening, exists between the granular and shearing plate. To understand the origin of the dependence of frictional sliding, we treat the granular system as a complex network, where granular particles are nodes and normal contact forces are weighted edges used to obtain insight into the interiors of granular matter. Community structures within granular property networks are detected under different shearing velocities in the steady state. Community parameters, such as the size of the largest cluster and average size of clusters, show significant monotonous trends in shearing velocity associated with the shear-rate dependence of granular friction. Then, we apply an instantaneous change in shearing velocity. A dramatic increase in friction is observed with a change in shearing velocity in the non-steady state. The community structures in the non-steady state are different from those in the steady state. Results indicate that the largest cluster is a key factor affecting the friction between the granular and shearing plate.

关键词： granular shear-rate friction community detection

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Generative Retrieval Meets Multi-Graded Relevance 38

Generative Retrieval Meets Multi-Graded Relevance

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Tang, Yubao Zhang, Ruqing Guo, Jiafeng de Rijke, Maarten Chen, Wei Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS China University of Chinese Academy of Sciences China University of Amsterdam Netherlands

Generative retrieval represents a novel approach to information retrieval. It uses an encoder-decoder architecture to directly produce relevant document identifiers (docids) for queries. While this method offers benefits, current approaches are limited to scenarios with binary relevance data, overlooking the potential for documents to have multi-graded relevance. Extending generative retrieval to accommodate multi-graded relevance poses challenges, including the need to reconcile likelihood probabilities for docid pairs and the possibility of multiple relevant documents sharing the same identifier. To address these challenges, we introduce a framework called GRaded Generative Retrieval (GR2). GR2 focuses on two key components: ensuring relevant and distinct identifiers, and implementing multi-graded constrained contrastive training. First, we create identifiers that are both semantically relevant and sufficiently distinct to represent individual documents effectively. This is achieved by jointly optimizing the relevance and distinctness of docids through a combination of docid generation and autoencoder models. Second, we incorporate information about the relationship between relevance grades to guide the training process. We use a constrained contrastive training strategy to bring the representations of queries and the identifiers of their relevant documents closer together, based on their respective relevance grades. Extensive experiments on datasets with both multi-graded and binary relevance demonstrate the effectiveness of GR. © 2024 Neural information processing systems foundation. All rights reserved.

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ICT at TREC 2019: Fair Ranking Track 28

ICT at TREC 2019: Fair Ranking Track

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28th Text REtrieval Conference, TREC 2019

作者： Wang, Meng Zhang, Haopeng Liang, Fuhuai Feng, Bin Zhao, Di CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

In this paper, we will introduce our work in the 2019 TREC fair ranking task. In temporal academic search, more and more people choose to pay attention to the fairness constraints of ranking. The purpose of this task is to provide fairness exposure to different groups of authors, where the definition of group is diverse, such as based on demographics, height, topics, etc. For this reason, the model we design should consider the fairness of the ranking results while ensuring the relevance of the ranking results. We will show how to use our model to achieve the relevance of query results, then adjust the overall ranking results according to the fairness of documents, and finally achieve the relevance and fairness of document ordering. This paper will introduce the framework and methods of the fair ranking system, as well as the experimental results. © 2019 28th Text REtrieval Conference, TREC 2019 - Proceedings. All Rights Reserved.

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Improved deduplication method based on variable-size sliding window

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content technology and its Applications 2011年第9期5卷 80-87页

作者： Wang, Can Qin, Zhiguang Yang, Lei Nie, Peng School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611731 China Network and Data Security Key Laboratory of Sichuan Province Chengdu 611731 China

To improve the deduplication performance while keep a reasonable metadata cost and time cost at the same time, a state deduplication method based on variable-size sliding window and a universal model of performance-analyzing for the deduplication methods are proposed. According to this method, the data object is divided into non-overlapped mini chunks based on its content firstly, and then a variable-size sliding window, which uses the mini chunks as its basic unit of movement, is used to identify the duplicate data blocks. Further more, different chunking strategies are used on the data changing regions and the non-changing regions respectively. The theoretical analyzing indicates that this method can achieve satisfying deduplication performance even with a relatively larger expected chunk size, because it can reduce the metadata cost effectively and identify smaller duplicate data blocks. The experimental results on real data show that the average compression ratio of the method can be increased 13.02% than SWC, which has the optimal deduplication performance among the current deduplication methods. Moreover, the average time cost of this method is reduced 97.45% than SWC. This method is suitable for the applications of mass data backup in network environment, which have more rigorous requirements on the deduplication performance and the time cost.

关键词： Metadata

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