图注意力网络(Graph Attention Network,GAT),它通过注意力机制(Attention Mechanism)来对邻居节点做聚合操作,实现了对不同邻居权重的自适应分配,从而大大提高了图神经网络模型的表达能力。

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本文提出了一个新的多粒度阅读理解框架,并且在NQ数据集上验证了其有效性。我们利用文档自身的层次结构特性,以四个粒度建模文档,并且同时考虑NQ中两个粒度答案的依赖关系。实验结果表明我们提出的方法是非常有效的,并且相比现有方法有了大幅度的提升。

整体系统架构,所有文档片段被独立的输入到模型中,最后汇总之后得到答案 我们针对这种NQ数据集提出了一个新的框架,整体系统架构如图3所示,我们将问题以及文档的每个片段独立的输入到模型中,通过BERT编码器进行编码,得到问题和文档片段的初步表示,然后用我们提出的图编码器用得到的表示进一步建模,最终得到一系列结构化的表示,汇总到答案选择模块得到答案。

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Robot control policies learned in simulation do not often transfer well to the real world. Many existing solutions to this sim-to-real problem, such as the Grounded Action Transformation (GAT) algorithm, seek to correct for or ground these differences by matching the simulator to the real world. However, the efficacy of these approaches is limited if they do not explicitly account for stochasticity in the target environment. In this work, we analyze the problems associated with grounding a deterministic simulator in a stochastic real world environment, and we present examples where GAT fails to transfer a good policy due to stochastic transitions in the target domain. In response, we introduce the Stochastic Grounded Action Transformation(SGAT) algorithm,which models this stochasticity when grounding the simulator. We find experimentally for both simulated and physical target domains that SGAT can find policies that are robust to stochasticity in the target domain

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Robot control policies learned in simulation do not often transfer well to the real world. Many existing solutions to this sim-to-real problem, such as the Grounded Action Transformation (GAT) algorithm, seek to correct for or ground these differences by matching the simulator to the real world. However, the efficacy of these approaches is limited if they do not explicitly account for stochasticity in the target environment. In this work, we analyze the problems associated with grounding a deterministic simulator in a stochastic real world environment, and we present examples where GAT fails to transfer a good policy due to stochastic transitions in the target domain. In response, we introduce the Stochastic Grounded Action Transformation(SGAT) algorithm,which models this stochasticity when grounding the simulator. We find experimentally for both simulated and physical target domains that SGAT can find policies that are robust to stochasticity in the target domain

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