Neural Machine Translation Flexibility via Conversion Conditions
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Solution Overview
Problem
Current information conversion methods using neural networks lack flexibility, requiring retraining when adjustments are needed and failing to incorporate external resources or new mapping relationships in a timely manner.
Innovation Solution
An information conversion method that encodes source information into a code, applies a preset conversion condition indicating a mapping relationship, and decodes the code to produce target information, allowing for more flexible and accurate translation by integrating a conversion condition into the neural machine translation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a trained neural network model is used for information conversion, then translation accuracy is improved, but flexibility deteriorates because retraining is required for any adjustments
Solution Approach 1:
The patent segments the translation system into two independent components: a pre-trained neural network model for accurate translation and a separate conversion condition module for flexible adjustments. This allows the model to maintain high translation accuracy while enabling flexibility through independent condition modifications without requiring retraining of the entire system.
Solution Approach 2:
The patent introduces conversion conditions as an intermediary layer between the source information and the neural network model. These conditions act as mediators that can be easily adjusted and added without affecting the trained model, thereby providing flexibility while preserving translation accuracy.
2Adaptability or versatility
If external resources and new mapping relationships are incorporated into the neural network model, then information conversion capability is improved, but training time and computational resources increase
Solution Approach 1:
The patent performs preliminary training of the neural network model once with comprehensive data, then stores the trained model for repeated use. Conversion conditions and external resources are incorporated as separate configurable parameters that do not require retraining, thereby eliminating repeated training time while maintaining enhanced conversion capability.
Solution Approach 2:
The patent makes the system dynamic by allowing conversion conditions to be added, modified, or removed without retraining. This dynamic configuration capability enables the system to adapt to new mapping relationships and external resources instantly, avoiding the time cost of retraining while improving information conversion capability.
Data Source
AI summary
Embodiments of this application include an information conversion method for translating source information. The source information is encoded to obtain a first code. A preset conversion condition is obtained. The preset conversion condition indicates a mapping relationship between the source information and a conversion result. The first code is decoded according to the source information, the preset conversion condition, and translated information to obtain target information. The target information and the source information are in different languages. Further, the translated information includes a word obtained through conversion of the source information into a language of the target information.