Streaming Neural Machine Translation with Predictive Token Modeling
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Solution Overview
Problem
Existing machine translation systems, particularly streaming neural machine translation (NMT), face challenges in determining the optimal between reading and writing operations to improve translation performance and speed.
Innovation Solution
The proposed solution involves an electronic apparatus equipped with a processor that acquires tokens from user voice inputs, processes them through a first neural network model to generate translated text, and uses a second neural network model to predict subsequent tokens, thereby optimizing the translation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If streaming NMT translates words in real time, then translation speed is improved, but translation performance deteriorates
Solution Approach 1:
The system performs preliminary actions by predicting future tokens before they are actually input. The prediction module generates probable subsequent tokens in advance, allowing the translation model to prepare and process translations more efficiently, thus improving both speed and performance in streaming scenarios
Solution Approach 2:
The system dynamically adjusts the translation process by continuously predicting future tokens and adapting to incoming input. The prediction module updates its predictions based on new tokens as they arrive, making the translation system flexible and adaptive to real-time input changes while maintaining high translation quality
2Manufacturing precision
If the system waits for complete input sentences, then translation performance is improved, but translation speed deteriorates
Solution Approach 1:
Instead of waiting for complete input, the system performs preliminary translation actions on available tokens immediately. The prediction module generates future token predictions in parallel, allowing the system to start translation before the full input is received, thus improving speed without significantly compromising performance
Solution Approach 2:
The system uses feedback from prediction accuracy to adjust its translation strategy. By monitoring whether predicted tokens match actual future inputs, the system can refine its prediction model and translation process, maintaining high performance while operating in real-time streaming mode
3Speed
If the system predicts subsequent tokens, then translation speed is improved, but computational complexity increases
Solution Approach 1:
The translation system is segmented into distinct functional modules: a prediction module for generating future tokens, a translation module for performing the actual translation, and a processing module for managing the streaming input. This segmentation allows each component to be optimized independently and reduces overall computational complexity by distributing tasks across specialized sub-systems
Data Source
AI summary
An electronic apparatus, including a microphone; a memory configured to store at least one instruction; and a processor configured to: acquire a first token corresponding to a first user voice input in a first language acquired through the microphone, acquire a first text in a second language by inputting the first token into a first neural network model, acquire a feature value corresponding to a predicted subsequent token, which is predicted to be uttered after the first token, by inputting the first text into a second neural network model, and based on a second token being acquired subsequent to the first token, acquire a second text in the second language by inputting the first token, the second token, the first text, and the feature value into the first neural network model.