Neural Network Translation Latency and Accuracy Trade-off
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Solution Overview
Problem
Current electronic translation devices suffer from poor real-time translation performance, with low accuracy and increased waiting times, making them impractical for everyday use.
Innovation Solution
An electronic device equipped with a processor and neural network models that input and process texts in multiple languages, using sentence similarity and suitability values to determine when to output translations, thereby reducing latency and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If translation is performed after the entire input text is completed, then translation accuracy is improved, but waiting time increases
Solution Approach 1:
The system performs preliminary translation actions by translating input texts as they are being entered rather than waiting for completion. The neural network model continuously processes and outputs translated text in real-time, enabling users to see translation results immediately as input is provided, thus reducing waiting time while maintaining accuracy through continuous processing
2Loss of time
If real-time translation is implemented, then waiting time is reduced, but translation accuracy deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms where the neural network model continuously evaluates and adjusts translation outputs based on the input text being processed in real-time. This feedback loop ensures that translation accuracy is maintained even during real-time processing by continuously refining the translation results as new input text becomes available
3Loss of time
If translation is performed sentence by sentence, then translation can start earlier, but overall translation time increases
Solution Approach 1:
The system maintains continuous useful action by continuously processing and translating input text as it is entered, without interruption or waiting for sentence completion. The neural network model operates continuously, translating each segment of input text immediately, which both enables early translation start and maintains high overall translation speed through uninterrupted processing
Data Source
AI summary
An electronic device includes a memory storing at least one instruction, and a processor configured to execute the at least one instruction to: input a first text into a first neural network model and acquire a first translation text; input the first translation text into a second neural network model and acquire a first liberal translation text; identify whether the first liberal translation text is to be outputted based on the first text and the first liberal translation text; output the first liberal translation text according to the identification result; based on a second text being received subsequently to the first text, input the first and second texts into the first neural network model and acquire a second translation text; and input the identification result and the second translation text into the second neural network model and acquire a second liberal translation text corresponding to the second translation text.