AI Translation Model for Mixed Language Text Accuracy
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Solution Overview
Problem
There is a need for an electronic device and method to accurately translate mixed language texts into single language texts, as existing systems struggle to efficiently handle multiple languages and maintain key word accuracy.
Innovation Solution
An electronic device equipped with a user interface and a processor that applies a mixed language text to an AI model trained on a corpus where words of multiple languages are mapped, allowing for the identification and display of a corresponding single language text, utilizing a corpus-based AI model for translation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing rule-based smart systems are used for translation, then system complexity is reduced, but translation accuracy and understanding of user tastes deteriorate
Solution Approach 1:
The patent replaces rule-based mechanical translation systems with deep learning-based AI systems that can understand and translate mixed language texts more accurately. The AI model learns patterns from training data rather than following predetermined rules, enabling better handling of complex multilingual scenarios.
Solution Approach 2:
The patent changes the fundamental parameter of the translation system from rule-based algorithms to neural network models with learnable parameters. This transformation enables the system to adapt to different language patterns and contexts, improving translation quality while managing complexity through automated model training.
2Reliability
If deep learning-based AI systems are used for translation, then translation accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary actions by pre-training the AI model on extensive multilingual corpora before actual translation tasks. This offline training phase prepares the model to handle various language combinations efficiently during runtime, reducing the computational burden during actual translation operations.
Solution Approach 2:
The patent uses trained AI models that can be copied and deployed across multiple devices or instances. Once a model is trained with significant computational resources, it can be replicated and used repeatedly without requiring the same level of computational power for each translation task.
3Measurement precision
If mixed language texts are translated using conventional methods, then processing speed is maintained, but key word accuracy and language nuance deteriorate
Solution Approach 1:
The patent applies local quality by focusing the AI model's attention on specific parts of the input text that require accurate translation, such as key words and phrases. The model can identify and prioritize important segments while maintaining overall translation quality, balancing precision with processing efficiency.
Data Source
AI summary
An electronic device and method for translating first text including a plurality of languages into second text including a single language from among the plurality of languages, is provided. The electronic device includes a user interface device configured to receive an input of a user regarding the first text and output the second text, a memory storing instructions, and a processor configured to execute the instructions to control the electronic device to identify the first text, apply the first text to an artificial intelligence (AI) model trained based on a corpus in which words of the plurality of languages are mapped to each other, identify the second text corresponding to the first text from the trained AI model, and display the second text.