Introduction

• The paper introduces a novel architecture that generates an output sequence such that the elements of the output sequence are discrete tokens corresponding to positions in the input sequence.
• Such a problem can not be solved using Seq2Seq or Neural Turing Machines as the size of the output softmax is variable (as it depends on the size of the input sequence).
• Link to the paper

Architecture

• Traditional attention-base sequence-to-sequence models compute an attention vector for each step of the output decoder and use that to blend the individual context vectors of the input into a single, consolidated attention vector. This attention vector is used to compute a fixed size softmax.
• In Pointer Nets, the normalized attention vector (over all the tokens in the input sequence) is normalized and treated as the softmax output over the input tokens.
• So Pointer Net is a very simple modification of the attention model.

Application

• Any problem where the size of the output depends on the size of the input because of which fixed length softmax is ruled out.
• eg combinatorial problems such as planar convex hull where the size of the output would depend on the size of the input.

Evaluation

• The paper considers the following 3 problems:
• Convex Hull
• Delaunay triangulations
• Travelling Salesman Problem (TSP)
• Since some of the problems are NP hard, the paper considers approximate solutions whereever the exact solutions are not feasible to compute.
• The authors used the exact same architecture and model parameters of all the instances of the 3 problems to show the generality of the model.
• The proosed Pointer Nets outperforms LSTMs and LSTMs with attention and can generalise quite well for much larger sequences.
• Interestingly, the order in which the inputs are fed to the system affects its performance. The authors discussed this apsect in their subsequent paper titled Order Matters: Sequence To Sequence for Sets