Generic NMT Model with Context-Aware Translation Memory
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Neural Machine Translation (NMT) models face challenges in providing accurate translations across diverse subject matter fields, as generalized models tend to generalize at the expense of quality, while specialized models require extensive training and resources, and are not scalable.
Innovation Solution
A system using a generic NMT model combined with a translation memory that updates based on user corrections, storing source and target language segments along with context information to adapt translations to specific contexts, improving accuracy iteratively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a generalized NMT model is trained using training data from a wide range of subject matter fields, then the model can translate across diverse fields, but translation quality deteriorates as the model is forced to generalize at the expense of accuracy
Solution Approach 1:
The system segments the translation task by maintaining a single generic NMT model while creating separate translation memory databases for different subject matter fields. Each field has its own translation memory containing domain-specific source-target text segment pairs, allowing the model to access specialized translation knowledge without requiring separate model training for each field.
Solution Approach 2:
The translation memory acts as an intermediary between the generic NMT model and the end user. It stores and retrieves domain-specific translation examples that modify and refine the output of the generic model, enabling context-specific translations without changing the underlying model structure or requiring extensive retraining.
2Measurement precision
If multiple specialized NMT models are trained for different subject matter fields, then translation accuracy improves for each field, but device complexity and resource requirements increase significantly
Solution Approach 1:
A single generic NMT model is designed to handle multiple subject matter fields universally. Instead of training separate specialized models for each field, the system uses one model that can translate across diverse domains, with translation memory databases providing domain-specific guidance when needed.
Solution Approach 2:
The system changes the parameters stored in translation memory databases rather than changing the model parameters themselves. By accumulating domain-specific translation examples in translation memories, the system adapts to different fields through data accumulation rather than model retraining, avoiding the complexity of multiple specialized models.
3Device complexity
If a generic NMT model is used without domain-specific adaptation, then device complexity remains low, but translation quality deteriorates in context-specific scenarios
Solution Approach 1:
The system performs preliminary organization of domain-specific translation data into structured translation memory databases before translation tasks are executed. These databases are pre-populated with source-target text segment pairs and contextual information for various subject matter fields, enabling the generic model to access relevant domain knowledge during translation without requiring complex adaptations.
Solution Approach 2:
The system implements feedback mechanisms where user corrections to machine-generated translations are stored in the translation memory databases. This feedback loop continuously enriches the domain-specific translation knowledge, allowing the system to improve context-specific accuracy over time while maintaining the simplicity of the generic model structure.
Data Source
AI summary
System and method for providing a computer-assisted translation from a source language to a target language, using a generic NMT model and a translation memory. An input text segment is received, and input context information is received. The input context information is indicative of circumstances in which the input text segment is used, the input text segment being in the source language. An estimated translation of the input text segment into the target language is calculated, using a generic neural machine translation “NMT” model for providing a generalised machine translation from the source language to the target language, and a translation memory comprising translation elements; each translation element comprising a source language text segment, a corresponding target language text segment. The estimated translation is provided to a user, for correction by the user. The translation memory is updated by storing the input text segment, the corrected translation, and the input context information, as a new translation element in the translation memory.