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Found 16 papers, 1 papers with code
Position-Aware Self-Attention based Neural Sequence Labeling
Sequence labeling is a fundamental task in natural language processing and has been widely studied.
Stack-VS: Stacked Visual-Semantic Attention for Image Caption Generation
Recently, automatic image caption generation has been an important focus of the work on multimodal translation task.
ReInceptionE: Relation-Aware Inception Network with Joint Local-Global Structural Information for Knowledge Graph Embedding
In this paper, we take the benefits of ConvE and KBGAT together and propose a Relation-aware Inception network with joint local-global structural information for knowledge graph Embedding (ReInceptionE).
A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment
In this paper, we propose a novel Contextual Alignment Enhanced Cross Graph Attention Network (CAECGAT) for the task of cross-lingual entity alignment, which is able to jointly learn the embeddings in different KGs by propagating cross-KG information through pre-aligned seed alignments.
基于层次化语义框架的知识库属性映射方法(Property Mapping in Knowledge Base Under the Hierarchical Semantic Framework)
面向知识库的自动问答是自然语言处理的一项重要任务, 它旨在对用户提出的自然语言形式问题给出精炼、准确的回复。目前由于缺少数据集、特征不一致等因素, 导致难以使用通用的数据和方法实现领域知识库问答。因此, 本文将“问题意图”视作不同领域问答可能存在的共同特征, 将“问题”与三元组知识库中“关系谓词”的映射过程作为问答核心工作。为了考虑多种层次的语义避免重要信息的损失, 本文分别将“基于门控卷积的深层语义”和“基于交互注意力机制的浅层语义”两个方面通过门控感知机制相融合。我们在NLPCC-ICCPOL 2016 KBQA数据集上的实验表明, 本文提出的方法与现有的基于CDSSM和BDSSM相比, 效能有明显的提升。此外, 本文通过构造天文常识知识库, 将问题与关系谓词映射模型移植到特定领域, 结合Bi-LSTM-CRF模型构建了天文常识自动问答系统。
基于Graph Transformer的知识库问题生成(Question Generation from Knowledge Base with Graph Transformer)
知识库问答依靠知识库推断答案需大量带标注信息的问答对, 但构建大规模且精准的数据集不仅代价昂贵, 还受领域等因素限制。为缓解数据标注问题, 面向知识库的问题生成任务引起了研究者关注, 该任务是利用知识库三元组自动生成问题。现有方法仅由一个三元组生成的问题简短且缺乏多样性。为生成信息量丰富且多样化的问题, 本文采用Graph Transformer和BERT两个编码层来加强三元组多粒度语义表征以获取背景信息。在SimpleQuestions上的实验结果证明了该方法有效性。
Hierarchical Neighbor Propagation With Bidirectional Graph Attention Network for Relation Prediction
Abstract—The graph attention network (GAT) [1] has started to become a mainstream neural network architecture since 2018, yielding remarkable performance gains in various natural language processing (NLP) tasks.
