NMT Domain And Noise Adapters for Robust Cross-Domain Translation
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Solution Overview
Problem
Neural machine translation models struggle with robustness issues due to noisy inputs, particularly when encountering real-world noise distributions that were not seen during training, and existing solutions fail to generalize well across domains and noise types.
Innovation Solution
A method involving a domain adapter trained on clean in-domain data and individual noise adapters trained on specific noise sources is injected into a pretrained neural machine translation model, enhancing its robustness to various noise modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real noisy data is used to fine-tune generic models, then robustness to realistic noise distributions is improved, but generalization to unseen domains and noise distributions deteriorates
Solution Approach 1:
The adapter is divided into two separate components: a domain adapter trained on clean in-domain data and multiple noise adapters trained on different noise sources. This segmentation allows each adapter to specialize in one aspect (domain or noise type) without interfering with the other, enabling independent optimization and better generalization to unseen combinations of domains and noise types.
Solution Approach 2:
The noise adapters are designed to be universally applicable across different domains. By training noise adapters on noisy data from various sources and making them domain-agnostic, the system achieves multi-functionality where the same noise adaptation mechanism can handle different domain-specific noise patterns, improving generalization to unseen domains.
2Productivity
If synthetic noise is used for training, then training efficiency is improved, but performance on real noise deteriorates
Solution Approach 1:
The system performs preliminary action by training on synthetic noise during the adapter training phase, then applies this pre-trained adapter to handle real noise during deployment. The domain adapter is first trained on clean data, then noise adapters are trained on synthetic noise and applied to improve robustness against real noise distributions without retraining the entire model.
3Device complexity
If domain adaptation and noise adaptation are combined in a single adapter, then model complexity is reduced, but adaptation performance deteriorates
Solution Approach 1:
The adaptation mechanism is segmented into separate domain adapter and noise adapter components. The domain adapter handles domain-specific features trained on clean in-domain data, while noise adapters handle noise-specific features trained on noisy data from various sources. This segmentation improves adaptation performance by allowing each component to specialize without the interference that would occur in a combined adapter.
Data Source
AI summary
Methods and systems for adapting a pretrained neural machine translation (NMT) model for translating text from a noisy input in a domain. A domain adapter injected into the pretrained NMT model is trained on clean in-domain data for adapting the pretrained NMT to the domain. Each of a plurality of individual noise adapters injected into the pretrained NMT model are trained on noisy input data from an individual noise source for adapting to the individual noise source.