Pointer-Token Sequence Modeling for Faster Seq2Seq Inference
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Solution Overview
Problem
Existing sequence-to-sequence machine learning models are computationally expensive and inefficient in processing and generating output sequences, particularly when dealing with large input sequences, which limits their application in real-time scenarios.
Innovation Solution
Implementing a sequence-to-sequence model that compresses input sequences using pointer tokens to represent repeated subsequences, allowing the model to process and generate compressed output sequences, thereby reducing computational requirements and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sequence-to-sequence models process complete token sequences, then sequence generation accuracy is maintained, but computational cost and processing time increase significantly
Solution Approach 1:
The patent applies preliminary compression to the input token sequence before processing it through the sequence-to-sequence model. By pre-compressing the sequence into a condensed representation with fewer tokens, the model processes less data while retaining essential information, thus reducing processing time without significantly compromising generation accuracy.
Solution Approach 2:
The patent extracts redundant and repetitive information from the original token sequence, keeping only the essential unique tokens in a compressed form. This extraction of core information allows the model to focus on processing meaningful content rather than repetitive patterns, improving efficiency while maintaining accuracy.
2Loss of information
If traditional sequence-to-sequence models process large input sequences, then comprehensive sequence information is captured, but memory requirements and computational resources increase
Solution Approach 1:
The patent performs preliminary compression of the input sequence into a condensed representation that retains essential information in a more compact form. This pre-processing step reduces the memory footprint of the input data before it enters the sequence-to-sequence model, allowing the model to work with less memory while preserving critical sequence information.
Solution Approach 2:
The patent creates a compressed copy of the original token sequence that preserves the essential information structure. Instead of processing the full original sequence, the model processes this compressed representation, which uses less memory but maintains the necessary information for accurate sequence generation.
3Productivity
If sequence compression using pointer tokens is implemented, then processing efficiency and speed improve, but model vocabulary complexity increases
Solution Approach 1:
The patent introduces pointer tokens as intermediary elements that reference previous tokens in the sequence. These pointer tokens act as mediators between the compressed representation and the original sequence structure, enabling efficient processing while managing vocabulary complexity through systematic reference mechanisms rather than requiring the model to learn all possible sequence variations.
Data Source
AI summary
A method performed by one or more computers and for generating an output token sequence from an input token sequence. The method comprises processing an input token sequence using a sequence-to-sequence machine learning model to generate an output token sequence. The sequence-to-sequence machine learning model has a vocabulary comprising primary tokens for representing token sequences and pointer tokens for representing pointers to token sequences. At least one of the input token sequence and the output token sequence is a compressed token sequence comprising a respective one or more pointer subsequences. Each pointer subsequence comprises one or more pointer tokens representing a pointer to a corresponding earlier subsequence of primary tokens in the compressed token sequence.