LSTM Encoder-Decoder for Sequence Prediction Accuracy
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Solution Overview
Problem
Existing systems face challenges in accurately converting input sequences, such as graphemes to phonemes, due to limitations in representing sequences in a different form effectively.
Innovation Solution
A system utilizing an encoder and decoder LSTM neural network to process input sequences, generating alternative representations and converting them into target sequences, employing beam search decoding for accurate sequence prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sequence conversion systems are used to convert input sequences to target sequences, then the system structure is simple, but the prediction accuracy of target sequences deteriorates
Solution Approach 1:
The system segments the sequence conversion task into two distinct neural network components: an encoder network that processes the input sequence and extracts features, and a decoder network that generates the target sequence from encoded representations. This segmentation allows each component to specialize in specific functions, improving overall prediction accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediate encoded representation as a mediator between the input sequence and target sequence. The encoder transforms the input sequence into this intermediate form, which the decoder then processes to generate the output. This intermediary representation captures essential features and relationships, enabling accurate sequence-to-sequence conversion that traditional direct systems cannot achieve.
2Measurement precision
If fixed-length representations are used for variable-length sequences, then the system complexity is reduced, but the accuracy of representing variable-length input sequences deteriorates
Solution Approach 1:
The encoder network dynamically processes variable-length input sequences by adapting its internal state representations to match the input length. Through recurrent connections and gating mechanisms, the encoder maintains dynamic hidden states that capture sequence information regardless of length, allowing accurate representation without requiring fixed-length constraints or complex padding operations.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating representations of input sequences. One of the methods includes obtaining an input sequence, the input sequence comprising a plurality of inputs arranged according to an input order; processing the input sequence using a first long short term memory (LSTM) neural network to convert the input sequence into an alternative representation for the input sequence; and processing the alternative representation for the input sequence using a second LSTM neural network to generate a target sequence for the input sequence, the target sequence comprising a plurality of outputs arranged according to an output order.