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指針生成網絡 Pointer-Generator《Get To The Point: Summarization with Pointer-Generator Networks》論文閱讀筆記
時間 2020-12-24
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Introduction 復現 上篇論文《(Copynet)Incorporating Copying Mechanism in Sequence-to-Sequence Learning》模型與此比較相似,主要用在摘要和對話系統,不過其網絡結構比較複雜,混合概率是兩者的和(而且目標詞屬於類別的打分是一個「分段函數」)。 最近看的指針生成網絡,其混合概率直接就是decoder端的softmax加上
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相關文章
1.
PGN: 指針生成網絡(Get To The Point: Summarization with Pointer-Generator Networks)
2.
【論文閱讀】Get to the point :summarization with pointer-generator networks
3.
[ACL2017] Get to The Point: Summarization with Pointer-Generator Networks
4.
Get to the point: summarization with pointer-generator networks閱讀
5.
Get to the pointer:Abstractvie summarization with pointer-generator networks(指向生成器網絡)---2017ACL
6.
論文閱讀: Going Deeper with Point Networks
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論文--閱讀翻譯筆記-Reducing the Dimensionality of Data with Neural Networks
8.
論文閱讀筆記:From Image-level to Pixel-level Labeling with Convolutional Networks
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論文閱讀筆記《Ranking Sentences for Extractive Summarization with Reinforcement Learning》
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