检索结果-南通市图书馆

Heterogeneous memory enhanced graph reasoning network for cross-modal retrieval

Science China(Information Sciences) 2022年第7期65卷 157-169页

作者： Zhong JI Kexin CHEN Yuqing HE Yanwei PANG Xuelong LI School of Electrical and Information Engineering Tianjin University Center for Optical Imagery Analysis and Learning Northwestern Polytechnical University

Cross-modal retrieval(CMR) aims to retrieve the instances of a specific modality that are relevant to a given query from another modality, which has drawn much attention because of its importance in bridging vision with language. A key to the success of CMR is to learn more discriminative and robust representations for both visual and textual instances to further reduce the heterogeneous discrepancy existing in different modalities. In this paper, we address this challenging issue by proposing a heterogeneous memory enhanced graph reasoning network, named HMGR, to connect the semantic correlations between vision and language.On the one hand, we design a novel dual-path network architecture to generate relationship enhanced global representations by employing modality-specific graph reasoning on extracted local features for each instance.In this way, the topological interdependencies of both visual and textual intra-instance local fragments are fully mined to achieve a deeper semantic understanding of the relationships between them. On the other hand,we focus on utilizing inter-instance semantic correlated knowledge to enhance the discriminability of the final learned representations, which is achieved by introducing a joint heterogeneous memory network to iteratively restore both visual and textual instance-level information. Through interacting with long-term contextual multimodal knowledge, an encouraging shared latent feature space for mitigating the heterogeneous gap across different modalities can be learned. Extensive experiments under both image-text retrieval and video-text retrieval scenarios on three benchmark datasets demonstrate the effectiveness of our proposed method.

关键词： cross-modal retrieval graph reasoning memory network visual semantic embedding image-text retrieval video-text retrieval

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Knowledge-aware path: interpretable graph reasoning in proactive dialogue generation

引用

The Journal of China Universities of Posts and Telecommunications 2021年第1期28卷 1-9页

作者： Sun Yinan Xu Yajing Li Si Guo Jun School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China

Proactive dialogue generates dialogue utterance based on a conversation goal and a given knowledge graph(KG). Existing methods combine knowledge of each turn of dialogue with knowledge triples by hidden variables, resulting in the interpretability of generation results is relatively poor. An interpretable knowledge-aware path(KAP) model was proposed for knowledge reasoning in proactive dialogue generation.KAP model can transform explicit and implicit knowledge of each turn of dialogue into corresponding dialogue state matrix, thus forming the KAP for dialogue history. Based on KAP, the next turn of dialogue state vector can be infered from both the topology and semantic of KG. This vector can indicate knowledge distribution of next sentence, so it enhances the accuracy and interpretability of dialogue generation. Experiments show that KAP model’s dialogue generation is closer to actual conversation than other state-of-the-art proactive dialogue models.

关键词： knowledge-aware path graph reasoning proactive dialogue system

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Local-to-Global Causal reasoning for Cross-Document Relation Extraction

引用

IEEE/CAA Journal of Automatica Sinica 2023年第7期10卷 1608-1621页

作者： Haoran Wu Xiuyi Chen Zefa Hu Jing Shi Shuang Xu Bo Xu Institute of Automation Chinese Academy of SciencesBeijing 100190China. School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190China

Cross-document relation extraction(RE),as an extension of information extraction,requires integrating information from multiple documents retrieved from open domains with a large number of irrelevant or confusing noisy texts.Previous studies focus on the attention mechanism to construct the connection between different text features through semantic similarity.However,similarity-based methods cannot distinguish valid information from highly similar retrieved documents well.How to design an effective algorithm to implement aggregated reasoning in confusing information with similar features still remains an open issue.To address this problem,we design a novel local-toglobal causal reasoning(LGCR)network for cross-document RE,which enables efficient distinguishing,filtering and global reasoning on complex information from a causal perspective.Specifically,we propose a local causal estimation algorithm to estimate the causal effect,which is the first trial to use the causal reasoning independent of feature similarity to distinguish between confusing and valid information in cross-document RE.Furthermore,based on the causal effect,we propose a causality guided global reasoning algorithm to filter the confusing information and achieve global reasoning.Experimental results under the closed and the open settings of the large-scale dataset Cod RED demonstrate our LGCR network significantly outperforms the state-ofthe-art methods and validate the effectiveness of causal reasoning in confusing information processing.

关键词： Causal reasoning cross document graph reasoning relation extraction(RE)

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：