Pointer Network (Ptr-Net)

From GM-RKB
(Redirected from Pointer Network Model)
Jump to navigation Jump to search

A Pointer Network (Ptr-Net) is a Sequence-to-Sequence Neural Network With Attention that produces output sequences using a content-based attention mechanism over input sequences.



References

2017

2015

2015 PointerNetworks Fig1.png
Figure 1:(a) Sequence-to-Sequence - An RNN (blue) processes the input sequence to create a code vector that is used to generate the output sequence (purple) using the probability chain rule and another RNN. The output dimensionality is fixed by the dimensionality of the problem and it is the same during training and inference in Sutskever et al.(2014). (b) Ptr-Net - An encoding RNN converts the input sequence to a code (blue) that is fed to the generating network (purple). At each step, the generating network produces a vector that modulates a content-based attention mechanism over inputs (Bahdanau et al., 2015, Graves et al., 2014). The output of the attention mechanism is a softmax distribution with dictionary size equal to the length of the input.