Neural Network Translation Model Error Correction
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Solution Overview
Problem
Automatic translation systems are prone to voice recognition errors, particularly in noisy environments or when similar words have different pronunciations, leading to degraded translation quality due to the limitations of current mechanical learning-based voice recognition technologies.
Innovation Solution
A neural network-based translation model is generated using a multi-task learning technique, which includes a first neural network to convert erroneous input text into abstract information, a second neural network to generate the correct translation, and a third neural network to correct errors, all learning simultaneously through a single deep neural network process, utilizing embedding vectors in a multi-dimensional vector space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mechanical learning-based voice recognition is used, then voice recognition capability is improved, but voice recognition errors occur in noisy environments or when similar words have different pronunciations
Solution Approach 1:
The patent segments the translation task into two separate neural network components: one dedicated to error correction and another for translation generation. This segmentation allows each component to specialize in its specific function, with the error correction network focusing solely on identifying and correcting voice recognition errors, while the translation network focuses on generating accurate translations, thereby resolving the contradiction between overall capability and precision.
Solution Approach 2:
The patent introduces an intermediary error correction process between voice recognition and translation. The error correction network acts as a mediator that receives the raw voice recognition output, corrects potential errors, and passes the corrected text to the translation network. This intermediary step prevents voice recognition errors from directly affecting translation quality, thus resolving the accuracy issue while maintaining the benefits of mechanical learning-based recognition.
2Measurement precision
If error correction and translation are performed separately, then translation quality is improved, but processing time and complexity increase
Solution Approach 1:
The patent merges the error correction network and translation network into a unified end-to-end trainable system. Although the networks are functionally segmented, they are trained together as a single integrated model, allowing gradients to flow through both components simultaneously during training. This merging enables joint optimization of error correction and translation tasks, improving translation quality while avoiding the time loss associated with completely separate processing pipelines.
Solution Approach 2:
The patent creates a multi-functional neural network system where the shared lower layers of the network serve both error correction and translation functions. The early layers extract general linguistic features that are useful for both tasks, while later layers specialize in task-specific operations. This universal base structure reduces redundant processing and decreases overall processing time while maintaining high translation quality through specialized output layers.
Data Source
AI summary
Disclosed herein are an apparatus and method for generating a translation model and an apparatus and method for automatic translation. A translation model generation apparatus and corresponding method include a learning data collector configured to collect an original text, an original text containing an error, and a translation of the original text as learning data, and a model generator that generates a translation model to simultaneously perform error correction and translation generation by enabling a neural network to learn based on the collected learning data.