Compositional De-Attention Networks

NeurIPS 2019 Yi TayAnh Tuan LuuAston ZhangShuohang WangSiu Cheung Hui

Attentional models are distinctly characterized by their ability to learn relative importance, i.e., assigning a different weight to input values. This paper proposes a new quasi-attention that is compositional in nature, i.e., learning whether to \textit{add}, \textit{subtract} or \textit{nullify} a certain vector when learning representations... (read more)

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