Text-to-Text Adapter for Online Offline Training
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Solution Overview
Problem
Machine translation systems face challenges in balancing data quality and processing speed, particularly when dealing with proprietary or classified field-specific data that cannot be disclosed for training, leading to inefficiencies in adaptation and retraining processes.
Innovation Solution
A two-part training approach is implemented, where generic training emphasizes accuracy and is completed beforehand, followed by faster, less accurate field-specific training by the customer, using a merge module to combine parameters without full retraining, employing a lightweight word alignment model and mathematical formulas to adapt the system efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If slow but accurate training technique is used for generic training, then translation accuracy is improved, but processing time increases
Solution Approach 1:
The training process is divided into two distinct phases: offline training that emphasizes accuracy using slow but precise techniques, and online training that emphasizes speed using faster techniques. This segmentation allows each phase to optimize for its specific goal without compromising the other.
Solution Approach 2:
The offline training is performed in advance to establish a solid foundational model with high accuracy. This preliminary action prepares the system so that subsequent online training can focus on speed and adaptation without sacrificing the quality established during offline training.
2Measurement precision
If field-specific training data is used to improve translation quality, then translation quality is improved, but data security requirements prevent data disclosure
Solution Approach 1:
An adapter component is introduced as an intermediary that enables field-specific adaptation without requiring access to or disclosure of sensitive training data. The adapter serves as a mediator between the base translation system and domain-specific requirements, allowing customization while maintaining data security.
Solution Approach 2:
Instead of requiring access to original field-specific training data, the system creates a copy or representation of the adaptation in the form of an adapter model. This adapter captures the essential field-specific characteristics without needing the actual sensitive data, thereby maintaining security while achieving translation quality improvement.
3Measurement precision
If complete retraining is performed to adapt to field-specific data, then translation accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The field-specific adaptation characteristics are extracted from the training process and isolated into a separate adapter component. This extraction allows the adapter to be trained independently and applied to the base system without requiring complete retraining of the entire translation system, thereby reducing processing time and computational resources.
Solution Approach 2:
Instead of performing complete retraining of the entire translation system, only the necessary partial training is conducted on the adapter component. This partial action is sufficient to achieve field-specific adaptation while avoiding the excessive computational cost of full system retraining.
Data Source
AI summary
An adapter for a text to text training. A main corpus is used for training, and a domain specific corpus is used to adapt the main corpus according to the training information in the domain specific corpus. The adaptation is carried out using a technique that may be faster than the main training. The parameter set from the main training is adapted using the domain specific part.