Multilingual Translation Adapters for Zero-Shot Language Pairs
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Solution Overview
Problem
Existing multilingual neural machine translation (MNMT) systems face challenges in zero-shot translation performance, particularly when translating between language pairs that have not been seen during training, leading to insufficient modeling capacity and off-target translations, and require retraining for new languages, which increases model size and complexity.
Innovation Solution
A parameter-efficient MNMT system using monolingual and bilingual adapter layers that are trained separately for each language, allowing zero-shot translation without retraining the entire network, and incorporating language-specific embedding layers for improved translation quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of considered languages is increased to improve zero-shot translation performance, then translation quality for low-resource languages is improved, but model size increases and artifacts such as off-target translation occur
Solution Approach 1:
The patent segments the translation model into a parent MNMT system and separate adapter modules for each language pair. Each adapter contains encoder and decoder components that can be independently trained and selected, allowing the system to handle multiple languages without increasing the core model size. This modular structure enables zero-shot translation by combining adapters from different language pairs while maintaining manageable model complexity.
Solution Approach 2:
The parent MNMT system serves as a universal base model that can work with any combination of language-specific adapters. The adapter modules are designed to be interchangeable and composable, allowing the same parent model to support multiple language pairs through different adapter combinations. This universality enables the system to generalize to unseen language pairs without retraining the entire model.
2Manufacturing precision
If adapter layers are trained pair-wise for each language direction, then translation quality for specific language pairs is improved, but the number of parameters and model size increases
Solution Approach 1:
The translation model is segmented into a shared parent system and separate adapter modules for each language pair. Each adapter contains encoder and decoder components that are trained specifically for their language pair, ensuring high translation quality. The segmentation allows independent training of adapters without requiring proportional increases in overall model parameters, as the parent system parameters are shared across all language pairs.
Solution Approach 2:
Each adapter module is specialized for a specific language pair with locally optimized parameters, while the parent model provides globally shared representations. This local quality approach ensures that each language pair receives tailored processing in its dedicated adapter, improving translation quality without requiring the entire model to be retrained or expanded proportionally for each language pair.
3Reliability
If conventional neural networks are adapted for new languages by retraining, then translation performance for new languages is improved, but training time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-training the parent MNMT model on multiple languages and preparing separate adapter modules for each language pair before deployment. When a new language pair is needed, the system only needs to train or select the relevant adapter modules rather than retraining the entire model from scratch. This preliminary preparation of modular components enables rapid adaptation to new languages with minimal additional training time.
Solution Approach 2:
The system implements dynamic adaptability by allowing adapter modules to be selectively activated and combined based on the source and target languages. When translating between languages, the system dynamically selects and activates only the relevant adapter modules for that language pair, rather than using a static, fully-retrained model. This dynamic approach enables efficient adaptation to new languages by composing adapters from previously trained components.
4Manufacturing precision
If language-aware normalization or back translation is used to improve zero-shot translation, then translation quality is improved, but device complexity and computational overhead increase
Solution Approach 1:
The patent merges language-specific adaptation capabilities into compact adapter modules that are integrated directly into the parent MNMT system architecture. Rather than using separate post-processing steps like back-translation or normalization, the language adaptation functionality is merged into the core translation pipeline through the adapter modules. This integration achieves zero-shot translation quality improvements while avoiding the additional computational overhead of separate processing stages.
Data Source
AI summary
Multilingual neural machine translation systems having monolingual adapter layers and bilingual adapter layers for zero-shot translation include an encoder configured for encoding an input sentence in a source language into an encoder representation and a decoder configured for processing output of the encoder adapter layer to generate a decoder representation. The encoder includes an encoder adapter selector for selecting, from a plurality of encoder adapter layers, an encoder adapter layer for the source language to process the encoder representation. The decoder includes a decoder adapter selector for selecting, from a plurality of decoder adapter layers, a decoder adapter layer for a target language for generating a translated sentence of the input sentence in the target language from the decoder representation.