Dynamic Translation Model Selection for Out-of-Vocabulary Words
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Solution Overview
Problem
Existing electronic devices face challenges in generating high-quality translated texts, particularly when the translation intention involves words not present in the training data.
Innovation Solution
The electronic device employs multiple translation models, including a first model using target lemma annotation and a second model using the placeholder method, to generate translated texts based on the presence or absence of the translation intention word in the training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single translation model is used, then the device complexity is reduced, but the translation quality deteriorates when translation intention words are not in training data
Solution Approach 1:
The system dynamically switches between different translation models based on whether the translation intention word exists in the training data. When the word exists, the first translation model is used; when it doesn't exist, the second translation model is used. This dynamic adaptation resolves the contradiction by adjusting the system configuration based on runtime conditions.
Solution Approach 2:
The system changes the parameter of model selection based on the presence or absence of the translation intention word in the training data. By detecting this parameter change, the system selects the appropriate translation model to maintain high translation quality while managing device complexity through conditional model deployment.
2Reliability
If multiple translation models are used, then the translation quality is improved, but the device complexity increases
Solution Approach 1:
The translation system is segmented into two specialized models: the first translation model for handling translation intention words present in training data, and the second translation model for handling words absent from training data. This segmentation allows each model to be optimized for specific scenarios, improving overall translation quality while managing complexity through functional division.
Solution Approach 2:
The processor acts as an intermediary that detects whether the translation intention word exists in the training data and routes the translation request to the appropriate model. This intermediary function manages the complexity of having multiple models by implementing a simple decision logic that selects the right model based on the training data check.
3Device complexity
If the first translation model is used for all cases, then the device complexity is reduced, but the translation accuracy deteriorates for words not in training data
Solution Approach 1:
The system performs a preliminary check to determine whether the translation intention word exists in the training data before selecting the translation model. This preliminary action prevents the first translation model from being used inappropriately for words not in training data, thereby maintaining translation accuracy while keeping the device complexity manageable through pre-assessment.
Solution Approach 2:
The system uses feedback from the training data check to determine model selection. By checking whether the translation intention word exists in the training data and using this feedback information, the system accurately selects the appropriate model, ensuring high translation accuracy for both in-vocabulary and out-of-vocabulary words.
4Measurement precision
If translation method is selected based on training data presence, then the translation accuracy is improved, but the processing time increases
Solution Approach 1:
The system performs a partial check limited to determining whether the translation intention word exists in the training data, rather than performing comprehensive analysis. This partial action provides sufficient information for model selection without excessive processing time, balancing translation accuracy with efficient processing.
Data Source
AI summary
An electronic device is disclosed. The electronic device comprises: a memory storing a first translation model configured to translate using a first translation method and a second translation model configured to translate using a second translation method; and at least one processor, comprising processing circuitry, individually and/or collectively, is configured to: based on original text and a translation intention being input, identify whether a word corresponding to the translation intention exists in learning data; based on a word corresponding to the translation intention existing in the learning data, generate first translated text of the original text based on the first translation model and the translation intention; and, based on a word corresponding to the translation intention not existing in the learning data, generate second translated text of the original text based on the second translation model and the translation intention.