Multi-domain Machine Translation with Dynamic Domain Adaptation
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Solution Overview
Problem
Current machine translation systems face inefficiencies due to the need for manual or experimental differentiation of in-domain and out-of-domain training data, leading to inaccurate data division and the requirement for multiple systems to translate across various domains, resulting in suboptimal resource utilization and translation quality.
Innovation Solution
A multi-domain machine translation system that clusters general training data into domains using unsupervised clustering processes like k-means, enabling dynamic domain adaptation at translation time and generating domain-specific models and weights, allowing a single system to translate across multiple domains efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual or experimental methods are used to differentiate in-domain and out-of-domain training data, then domain-specific training data can be obtained, but the determination accuracy is poor and resource consumption increases
Solution Approach 1:
The system performs self-service by automatically determining domain characteristics through unsupervised clustering algorithms (e.g., k-means) applied to training data features. This eliminates manual annotation while achieving accurate domain differentiation, resolving the contradiction between determination accuracy and resource consumption
Solution Approach 2:
The patent replaces manual mechanical classification processes with automated computational clustering algorithms. The system uses feature extraction and unsupervised learning to automatically partition training data into domains, substituting human effort with algorithmic processing that is both accurate and scalable
2Manufacturing precision
If independent machine translation systems are built for each domain, then translation quality for specific domains is improved, but computing resource efficiency deteriorates
Solution Approach 1:
The patent implements a universal machine translation system that can handle multiple domains through dynamic domain adaptation. A single system is trained on multi-domain data and can adapt to different domains at translation time by selecting appropriate domain-specific parameters and features, eliminating the need for separate systems while maintaining translation quality
Solution Approach 2:
The system employs dynamic domain adaptation mechanisms that allow it to flexibly adjust to different domains during translation. By using unsupervised domain clustering and dynamic parameter selection, the system can adapt its behavior based on the input domain without requiring retraining or separate system instances, thus improving resource efficiency while preserving translation quality
3Productivity
If a single machine translation system is used for multiple domains, then resource efficiency is improved, but translation quality for specific domains deteriorates
Solution Approach 1:
The patent applies local quality by enabling the single system to use domain-specific features, parameters, and clustering results for different domains. Each domain receives customized processing tailored to its characteristics, ensuring high translation quality while maintaining the efficiency benefits of a unified system architecture
Solution Approach 2:
The system achieves domain-specific translation quality through dynamic parameter changes. By adjusting domain-specific parameters, feature weights, and clustering configurations based on the detected domain, the single system can optimize its performance for each domain without sacrificing resource efficiency
Data Source
AI summary
A machine translation system capable of clustering training data and performing dynamic domain adaptation is disclosed. An unsupervised domain clustering process is utilized to identify domains in general training data that can include in-domain training data and out-of-domain training data. Segments in the general training data are then assigned to the domains in order to create domain-specific training data. The domain-specific training data is then utilized to create domain-specific language models, domain-specific translation models, and domain-specific model weights for the domains. An input segment to be translated can be assigned to a domain at translation time. The domain-specific model weights for the assigned domain can be utilized to translate the input segment.