Client-Side Decoder for Interactive Machine Translation Latency
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Solution Overview
Problem
Interactive machine translation systems face significant latency issues due to network communication between client and server devices, which can lead to outdated translation suggestions and decreased user productivity.
Innovation Solution
By distributing computational load between client and server devices, using pre-computation and caching of translation processes, and employing heuristics to avoid re-computation of unchanged parts of translations, the system minimizes latency and improves the speed of displaying translation suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine translation computation is performed on server devices, then translation accuracy and computational resources are improved, but network latency increases and translation suggestions become outdated
Solution Approach 1:
The translation computation is segmented between client and server devices. The client device performs local computation for translation suggestions while the server device provides computational resources and models. This segmentation allows the system to reduce network latency by performing computation locally while maintaining access to server resources for accuracy.
Solution Approach 2:
Translation models and computational frameworks are pre-loaded on client devices before actual translation tasks. This preliminary action enables the client to perform translation computation locally without waiting for real-time data transfer from the server, significantly reducing network latency while maintaining translation accuracy through pre-fetched models.
2Loss of time
If full machine translation system is run on client device, then network latency is reduced, but device memory and computational resources are insufficient
Solution Approach 1:
The system extracts only the essential translation computation functionality to the client device while maintaining the full translation system on the server. The client device runs a simplified version that performs local computation for immediate translation suggestions, while the server provides the complete translation system with full resources for accurate computation.
Solution Approach 2:
The client device performs partial translation computation locally rather than the full system. This partial action is sufficient for generating translation suggestions quickly with low latency, while the server compensates for any computational limitations by providing the full translation system resources when needed.
3Productivity
If translation suggestions are updated frequently to match user input speed, then translation relevance is improved, but computational time increases and latency worsens
Solution Approach 1:
The system dynamically adjusts the frequency and extent of translation recomputation based on user input characteristics. When users type quickly, the system performs more frequent updates to maintain relevance. When input is slower, the system reduces computation frequency to save computational time. This dynamic adaptation allows the system to balance productivity with computational resource consumption.
Solution Approach 2:
The system changes computational parameters such as the level of detail in translation recomputation and update frequency based on real-time conditions. By adjusting these parameters dynamically, the system can provide fast updates when needed while reducing computational time when the user input rate is lower, thus balancing productivity with computational efficiency.
Data Source
AI summary
A computer-implemented method is programmed to support efficient and rapid generation of machine translation suggestions on client devices. Network latency is substantially reduced or eliminated by separating certain aspects of the translation workload across multiple classes of tasks, including final neural network output, between a client device and server device. The client device and server device may be connected such that a decoder portion of a machine translation system may be downloaded onto the client device, along with an initial translation suggestion and encoder outputs associated with a document, which document is in a source language to be translated into a target language. The initial translation suggestion may be replaced by an updated machine translation suggestion as a user inputs text in the target language called a prefix. This updated machine translation is generated on the client-side decoder using the previously-downloaded encoder outputs as input and the prefix as constraint.