Sequence Model Encoder With Temporal Module
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Solution Overview
Problem
Existing sequence models, such as the Transformer model, rely solely on self-attention networks to calculate dependency relationships between sequence elements, leading to poor task execution effects, including translation errors and omissions.
Innovation Solution
The proposed solution involves a sequence model processing method that incorporates both a self-attention encoder and a temporal encoder on the encoder side. The temporal encoder performs time series modeling to generate time series information, which is combined with the self-attention encoding results to improve the modeling capability of the sequence model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If only self-attention encoder is used to calculate dependency relationships, then the model structure remains simple, but the task execution effect deteriorates with translation errors and omissions
Solution Approach 1:
The patent combines self-attention encoding and temporal encoding into a unified encoder architecture. The self-attention encoder captures global dependencies while the temporal encoder captures sequential relationships, and their outputs are merged to produce comprehensive encoding results that improve translation accuracy without excessive structural complexity
Solution Approach 2:
The encoder uses a composite approach by integrating two different encoding mechanisms (self-attention and temporal encoding) into a single system. This composite structure leverages the strengths of both approaches: parallel processing capability of self-attention and sequential modeling capability of temporal encoding, resulting in improved task execution effect
2Measurement precision
If temporal encoder is added to perform time series modeling, then the modeling capability improves, but the device complexity increases
Solution Approach 1:
The encoder is segmented into two functional modules: self-attention encoder for global dependency capture and temporal encoder for sequential relationship capture. Each module has a specific function, allowing the system to achieve enhanced modeling capability while maintaining modular structure that manages complexity
Solution Approach 2:
Both the self-attention encoder and temporal encoder process the same input sequence but extract different types of features. The temporal encoder serves multiple purposes: capturing sequential dependencies, modeling time series information, and complementing the self-attention mechanism, thereby justifying the added complexity through multi-functionality
Data Source
AI summary
Embodiments of this application disclose a method for processing a word sequence using a sequence model and apparatus, to improve a task execution effect of a sequence model. The method includes: encoding a source sequence of words of a first language by using an encoder side of the sequence model, to obtain a first encoding result and a second encoding result; inputting a target sequence of words of the first language, the first encoding result, and the second encoding result into a decoder side of the sequence model to obtain a second sequence of words of a second language, and outputting the second sequence of words in the second language.