Hypernetwork-Generated Model Parameters for Machine Translation
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Solution Overview
Problem
Current machine translation methods require an increasing number of models to handle multiple language pairs, leading to inefficiencies in memory usage and operational costs, especially when translating between less frequently used languages.
Innovation Solution
The proposed method employs a hypernetwork to generate model parameters for encoders and decoders, allowing for the reuse of models across languages and reducing the need for storing multiple language-specific models, thereby optimizing memory usage and generating models only when needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple language-specific models are stored to handle multiple language pairs, then translation capability is improved, but memory usage and operational costs increase
Solution Approach 1:
The patent implements a universal encoder model that can process multiple source languages and a universal decoder model that can generate multiple target languages. Instead of storing separate encoder-decoder model pairs for each language pair, the system uses shared encoder and decoder models that are trained on multi-lingual data, enabling one model to serve multiple language translation functions simultaneously.
Solution Approach 2:
The patent merges multiple language-specific encoder models into a single multi-lingual encoder model and multiple language-specific decoder models into a single multi-lingual decoder model. This consolidation reduces the total number of models stored in memory while maintaining the ability to translate between multiple language pairs through the shared models.
2Adaptability or versatility
If multiple language-specific models are stored to handle multiple language pairs, then translation capability is improved, but operational costs increase
Solution Approach 1:
The universal encoder and decoder models can handle multiple language pairs simultaneously, reducing the need to load and execute multiple separate models. This decreases computational overhead and energy consumption during translation operations, as the same models are reused across different language pairs rather than invoking separate model instances.
Solution Approach 2:
The system dynamically loads only the necessary language-specific components or configuration parameters from the shared models when a particular language pair is needed, rather than permanently storing and actively maintaining multiple complete model sets in memory. This reduces operational memory access costs and energy consumption.
3Reliability
If language-specific models are stored for each language pair, then translation accuracy is maintained, but device complexity increases
Solution Approach 1:
The system uses a unified multi-lingual encoder and decoder architecture that maintains translation accuracy across multiple language pairs through shared learned representations. This universal approach simplifies model management by eliminating the need to separately train, store, and manage multiple language-specific model pairs, reducing system complexity while preserving translation quality.
Solution Approach 2:
The system dynamically adapts the shared encoder and decoder models to different language pairs through mechanism such as language-specific embeddings or adaptive parameter adjustment, allowing the same model structure to flexibly handle different languages without requiring separate static models for each language pair.
Data Source
AI summary
A machine translation method includes using an encoder of a source language to determine a feature vector from a source sentence expressed in the source language, using an attention model of a target language to determine context information of the source sentence from the determined feature vector, and using a decoder of the target language to determine a target sentence expressed in the target language from the determined context information.