Neural Machine Translation Model Adaptation via Vector Index Retrieval
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Solution Overview
Problem
Neural Machine Translation (NMT) models face challenges when applied to specific fields, requiring re-training and parameter adjustments to achieve accurate translations, and existing methods lack generalization when the input information does not match pre-stored attribute-value pairs.
Innovation Solution
The method involves obtaining a first hidden state vector and probability distribution from a pre-trained translation model, retrieving a second hidden state vector from a vector index library based on a predetermined condition, and fusing these distributions to generate a fused probability distribution, which is then used to determine a translation result, allowing the model to adapt to specific fields without re-training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If NMT models are applied to specific fields, then translation accuracy improves, but re-training and parameter adjustments are required
Solution Approach 1:
The patent pre-trains the NMT model on general corpus data before deployment. This preliminary training establishes a baseline model that can handle general translation tasks, eliminating the need for complete re-training when applied to specific fields. The model is prepared in advance with general knowledge, and only requires lightweight adaptation for domain-specific applications.
Solution Approach 2:
The patent adjusts specific parameters and probability distributions of the pre-trained model to adapt to domain-specific characteristics. Instead of re-training the entire model, the system modifies parameters such as probability distributions for domain-specific terms, allowing the model to achieve high translation accuracy in specific fields while retaining the general capabilities of the pre-trained model.
2Productivity
If NMT models use pre-stored attribute-value pairs, then translation speed improves, but generalization capability deteriorates
Solution Approach 1:
The patent dynamically adjusts probability distribution parameters based on the input context rather than relying on fixed pre-stored attribute-value pairs. The system modifies the probability distribution for target words based on the semantic context and domain characteristics, enabling the model to generalize to unseen translations while maintaining efficient inference speed through parameter-based adaptation rather than exhaustive pattern matching.
3Reliability
If NMT models are re-trained for specific fields, then field performance improves, but computational resources increase
Solution Approach 1:
The patent performs comprehensive training on general corpus data in advance, creating a pre-trained model that already possesses broad linguistic knowledge. This preliminary action eliminates the need for resource-intensive re-training when deploying to specific fields, as the model can be efficiently adapted through parameter adjustment rather than complete re-training, significantly reducing computational resource requirements.
Solution Approach 2:
The patent creates a pre-trained model that serves as a reusable template for multiple domain-specific applications. Instead of training separate models for each field, the system copies the pre-trained model and performs lightweight domain adaptation, reducing the overall computational resources required across multiple deployments while maintaining high field performance.
Data Source
AI summary
A method, apparatus and electronic device for information processing. The method includes: obtaining a first hidden state vector obtained by inputting information to be translated that is expressed in a source language into a pre-trained first translation model, and a first probability distribution that the first hidden state vector is predicted as respective words in a predetermined vocabulary; obtaining, from a vector index library of a target language, at least one of target index term that satisfies a predetermined condition with the first hidden state vector; determining a second probability distribution that the second hidden state vector is predicted as the respective words in the predetermined vocabulary; determining a second probability distribution of the second hidden state vector; fusing the first and second probability distribution to obtain a fusion probability distribution; and determining a translation result with the fusion probability distribution.