Simultaneous Translation with Controllable Latency via Prefix-to-Prefix Framework
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Solution Overview
Problem
Existing simultaneous translation systems face challenges in achieving low latency and high translation quality due to word order differences between source and target languages, leading to excessive latency and cognitive burden on users, and are often overcomplicated with multiple components and training difficulties.
Innovation Solution
A prefix-to-prefix framework with a wait-k policy that predicts target words using source sentence prefixes, allowing for arbitrary latency control and integrating anticipation and translation in a single model, applied to both Recurrent Neural Network (RNN) and Transformer models, along with a new latency metric called Average Lagging to evaluate synchronization with the speaker.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If full-sentence translation is used to handle word order differences, then translation accuracy is improved, but latency increases to at least one sentence
Solution Approach 1:
The patent segments the translation process into incremental steps, translating sentence prefixes rather than waiting for complete sentences. This allows translation to proceed in chunks (e.g., first 50% of source sentence), reducing latency while maintaining reasonable accuracy through progressive refinement as more source text becomes available.
Solution Approach 2:
The system performs preliminary translation actions on source sentence prefixes before the complete sentence is available. By anticipating and translating early portions of the source text, the system reduces overall latency while preparing for subsequent corrections or refinements when more context becomes available.
2Loss of time
If anticipation of future source words is implemented, then latency is reduced, but system complexity increases with multiple components and training difficulties
Solution Approach 1:
The patent merges anticipation and translation into a single unified neural network model rather than using separate components. This integration simplifies the system architecture and training process while still achieving low latency through the model's ability to predict future source words and translate prefixes effectively.
Solution Approach 2:
The single neural network model performs multiple functions: it handles both anticipation of future source words and translation of current prefixes. This multi-functional approach eliminates the need for separate prediction and translation components, reducing system complexity while maintaining low latency performance.
3Productivity
If simultaneous translation is implemented to translate concurrently with source speech, then user engagement is improved, but translation quality deteriorates due to incomplete context
Solution Approach 1:
The system dynamically adjusts the translation process based on available source text. It translates prefixes when they become available (improving engagement) while incorporating refinements as more context arrives (maintaining quality). This dynamic approach allows concurrent translation without sacrificing accuracy.
Solution Approach 2:
The system uses feedback from subsequent source words to refine earlier translations. As more of the source sentence becomes available, the model can correct or improve previous prefix translations, ensuring quality maintenance even though translation begins before complete context is available.
Data Source
AI summary
Presented herein are embodiments of a prefix-to-prefix framework for simultaneous translation that implicitly learns to anticipates in a single translation. Within these frameworks are effective “wait-k” policy model embodiments that may be trained to generate a target sentence concurrently with a source sentence but lag behind by a predefined number of words. Embodiments of the prefix-to-prefix framework achieve low latency and better quality when compared to full-sentence translation in four directions: Chinese↔English and German↔English. Also presented herein is a novel latency metric that addresses deficiencies of previous latency metrics.