Encoder-Decoder Semantic Analysis for Parallel Processing Efficiency
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Solution Overview
Problem
Current natural language processing methods using recurrent neural networks for semantic analysis are inefficient due to low computation efficiency and long training times, especially when dealing with long sentences or large datasets, leading to poor processing speed and accuracy in human-machine interaction applications.
Innovation Solution
The proposed method employs an encoder-decoder model with convolutional neural networks and deep neural networks to improve computation efficiency by using sentence and word identifiers, allowing for parallel processing and reducing the number of calculation steps required for semantic analysis, thereby shortening training time and enhancing processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If recurrent neural network models are used for semantic analysis, then semantic analysis capability is achieved, but computation efficiency is low and training time is long
Solution Approach 1:
The patent segments the semantic analysis task into multiple parallel processing streams using sentence identifiers and word identifiers. Each identifier is processed independently through separate neural network paths, allowing parallel computation instead of sequential processing, thereby improving computation efficiency and reducing training time.
Solution Approach 2:
The patent introduces identifier-based indexing as an additional dimension for organizing and processing semantic data. By mapping sentences and words to unique identifiers and processing these identifiers through neural networks, the system enables parallel computation across multiple dimensions simultaneously, significantly improving processing efficiency.
2Speed
If recurrent neural network models are used for semantic analysis, then semantic understanding is achieved, but processing speed is slow
Solution Approach 1:
The model is segmented into multiple independent processing paths that operate in parallel. Each path processes specific identifier types (sentence identifiers, word identifiers) independently, enabling simultaneous computation that increases processing speed without requiring a single complex sequential model.
Solution Approach 2:
The patent introduces identifier vectors as intermediary representations between input text and semantic analysis. These identifiers serve as compact mediators that capture essential semantic information, allowing faster processing compared to direct recurrent neural network processing of full text sequences.
3Productivity
If traditional neural network methods are used, then semantic analysis is performed, but the number of calculation steps is large
Solution Approach 1:
The calculation process is segmented into parallel operations on identifier vectors rather than sequential processing of complete sentences. This segmentation reduces the total number of calculation steps by enabling simultaneous computation across multiple identifier paths, directly improving calculation efficiency and reducing training time.
Data Source
AI summary
A semantic analysis method includes: inputting a word vector of each word in each sample sentence in a dialog flow into an encoder model, to obtain a sentence vector representing semantics of the sample sentence; inputting the sentence vector into a first decoder model corresponding to each context sentence of the sample sentence and a second decoder model corresponding to each word of the sample sentence, to obtain a first identifier corresponding to the context sentence and a second identifier corresponding to the word; obtaining a probability of the first identifier and a probability of the second identifier, and determining a value of a target function; performing parameter training on the encoder model according to the value of the target function; and inputting a word vector of each word in a test sentence into the trained encoder model, to obtain a sentence vector representing semantics of the test sentence.