Denoising Adapters for Multilingual Translation Without Back-Translation
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Solution Overview
Problem
Current multilingual unsupervised neural machine translation (mUNMT) methods rely on computationally costly back-translation and require significant design effort, especially in large-scale setups, and they are limited by the need for high-quality in-domain monolingual data, particularly for resource-rich languages.
Innovation Solution
Insert denoising adapters into a neural sequence-to-sequence (seq2seq) model's encoder and decoder, train these adapters on language-specific denoising tasks using monolingual data, and fine-tune the cross-attention weights with parallel data to adapt the model for multilingual translation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If back-translation is used in multilingual unsupervised NMT, then translation quality is improved, but computational cost increases significantly
Solution Approach 1:
The model is segmented into multiple language-specific towers, each handling a specific language pair. This segmentation allows independent training and optimization of each language pair without requiring computationally expensive back-translation for all language pairs, reducing overall computational cost while maintaining translation quality.
Solution Approach 2:
The patent implements a universal encoder that serves multiple language pairs simultaneously, making it multi-functional. This universal encoder can be shared across different language towers, reducing redundant computation and training requirements while maintaining the ability to handle diverse language pairs effectively.
2Device complexity
If a single shared model is used for multiple language pairs, then model maintenance is simplified, but cross-lingual knowledge transfer is limited
Solution Approach 1:
The patent employs a nested architecture where language-specific towers are nested within a universal encoder framework. Each language tower contains language-specific parameters while sharing the universal encoder, creating a hierarchical structure that enables both simplified maintenance (through modular towers) and effective knowledge transfer (through shared encoder).
Solution Approach 2:
The patent transitions from a flat single-model architecture to a multi-dimensional hierarchical structure with universal encoder at one level and language-specific towers at another level. This dimensional change allows simultaneous achievement of model simplicity and cross-lingual knowledge transfer capability.
3Manufacturing precision
If denoising adapters are inserted for each language, then unsupervised translation performance is improved, but model complexity increases
Solution Approach 1:
The patent applies local quality by inserting denoising adapters only at specific critical positions within the transformer architecture (such as after certain encoder/decoder layers) rather than uniformly throughout. This localized approach improves unsupervised translation performance while minimizing the increase in model complexity compared to universal insertion.
Solution Approach 2:
The patent implements partial action by selectively applying denoising adapters to only certain language towers or specific layers rather than all languages and all layers. This partial application achieves sufficient performance improvement for unsupervised translation while controlling the overall complexity increase.
Data Source
AI summary
Methods and systems for unsupervised training for a neural multilingual sequence-to-sequence (seq2seq) model. Denoising adapters for each of one or more languages is inserted into an encoder and/or a decoder of the seq2seq model. Parameters of the one or more denoising adapters are trained on a language-specific denoising task using monolingual text for each of the one or more languages. Cross-attention weights of the seq2seq model with the trained denoising adapter layers are fine-tuned on a translation task in at least one of the one or more languages with parallel data.