Neural Network Pointer Mechanism for Variable Output Sequences
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Solution Overview
Problem
Existing systems face challenges in generating output sequences from input sequences, particularly when the number of possible outputs varies with the length of the input sequence, and in tasks where outputs need to be selected from the inputs themselves, such as sorting or generating geometric representations.
Innovation Solution
A neural network system comprising an encoder RNN, an output selection subsystem, and a decoder RNN processes input sequences to generate output sequences by selecting inputs based on softmax outputs, allowing for variable-length outputs and tasks like sorting, convex hull, Delaunay triangulation, and Hamiltonian cycles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a neural network system uses a fixed vocabulary for output selection, then the system structure is simple and easy to implement, but it cannot handle tasks where the number of possible outputs depends on the input sequence length
Solution Approach 1:
The patent applies dynamics by making the output vocabulary size dynamic rather than fixed. The system adapts the number of possible outputs based on the input sequence length, allowing the network to handle variable-sized output spaces. This is achieved through mechanisms that determine output vocabulary size based on input characteristics, enabling the system to flexibly adjust between different task requirements without being constrained by a predetermined fixed vocabulary.
2Adaptability or versatility
If the system generates outputs selected from input sequences (e.g., sorting tasks), then task flexibility increases, but the complexity of selecting appropriate outputs from variable-length inputs increases
Solution Approach 1:
The patent employs an intermediary mechanism in the form of pointer outputs that indirectly select elements from the input sequence. Rather than directly manipulating input elements, the system generates pointer representations that reference input positions. These pointers serve as intermediaries that simplify the selection process for tasks like sorting and geometric operations, allowing the network to select outputs from variable-length inputs through a standardized pointer-based interface rather than direct element manipulation.
3Adaptability or versatility
If the system handles input sequences of varying lengths, then adaptability to different input sizes is improved, but the difficulty of maintaining consistent output generation increases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the output vocabulary size based on input sequence length. Rather than using a fixed output space, the system modifies the number of possible outputs as a parameter according to the specific input being processed. This parameter adaptation allows the network to maintain consistent output generation reliability across variable-length inputs by ensuring the output space is appropriately sized for each specific task instance.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output sequences from input sequences. One of the methods includes obtaining an input sequence having a first number of inputs arranged according to an input order; processing each input in the input sequence using an encoder recurrent neural network to generate a respective encoder hidden state for each input in the input sequence; and generating an output sequence having a second number of outputs arranged according to an output order, each output in the output sequence being selected from the inputs in the input sequence, comprising, for each position in the output order: generating a softmax output for the position using the encoder hidden states that is a pointer into the input sequence; and selecting an input from the input sequence as the output at the position using the softmax output.