Neural Network Sentence Evaluation for Translation Accuracy
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Solution Overview
Problem
Existing sentence evaluation methods, such as BLEU, struggle to accurately assess the meaning of translated sentences, leading to difficulties in implementing human-like subjective grading, as they rely on word counting rather than semantic evaluation.
Innovation Solution
A sentence evaluation apparatus and method utilizing machine learning with deep neural networks, including encoders and a fully connected layer, to recognize and compare translated sentences against reference sentences, generating evaluation information based on similarity and dissimilarity analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If word counting methods (BLEU) are used for sentence evaluation, then the evaluation process is simple and fast, but the accuracy of meaning assessment deteriorates
Solution Approach 1:
The patent replaces the mechanical word counting system (BLEU) with a neural network-based semantic recognition system. The encoder-decoder architecture with attention mechanisms processes sentence meanings rather than merely counting matching words, enabling accurate semantic evaluation while maintaining computational efficiency through learned representations.
Solution Approach 2:
The patent changes the evaluation parameters from discrete word counts to continuous semantic representations. By transforming sentences into vector embeddings through encoders and comparing them in a continuous semantic space, the system achieves nuanced meaning assessment that goes beyond simple word overlap metrics.
2Measurement precision
If deep neural networks with multiple encoders are used for semantic evaluation, then the accuracy of meaning assessment is improved, but the device complexity increases
Solution Approach 1:
The patent segments the evaluation system into distinct functional modules: first encoder for source sentence, second encoder for reference sentence, attention mechanisms for weighted feature extraction, and decoder for evaluation output. This modular segmentation makes the complex neural network system more manageable and interpretable while maintaining high assessment accuracy.
Solution Approach 2:
The patent introduces attention mechanisms as intermediary components between the encoders and decoder. These attention layers act as mediators that selectively weight important features from the encoded representations, simplifying the overall computation by focusing on relevant semantic elements rather than processing all features equally.
3Productivity
If automated evaluation systems are implemented, then the productivity of translation assessment is improved, but the ability to perform human-like subjective grading deteriorates
Solution Approach 1:
The patent changes the evaluation parameters to include multiple dimensions of quality assessment (fluency, adequacy, naturalness) rather than a single automated score. The neural network is trained to predict human subjective ratings by learning from paired data of translated sentences and human evaluation scores, enabling the automated system to replicate human grading patterns.
Solution Approach 2:
The patent incorporates feedback mechanisms where the neural network learns from human evaluation data and continuously improves its assessment accuracy. By training on large datasets of translated sentences with associated human subjective ratings, the system receives feedback that shapes its evaluation criteria to better align with human judgment standards.
Data Source
AI summary
A sentence evaluation apparatus evaluates a sentence which is input. The sentence evaluation apparatus includes an acquisition device and a processor. The acquisition device acquires information indicating a first input sentence and information indicating a second input sentence. The processor executes information processing on the information acquired by the acquisition device, using an algorithm based on machine learning. The processor includes a first encoder that recognizes the first input sentence and a second encoder that recognizes the second input sentence, in the algorithm based on the machine learning. The processor generates evaluation information indicating evaluation on the first input sentence with reference to the second input sentence, based on a result of recognition by the first encoder on the first input sentence and a result of recognition by the second encoder on the second input sentence.