Natural Language Correction System for Machine Translation Accuracy
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Solution Overview
Problem
Machine translation engines are hindered by errors in source content items, leading to reduced effectiveness in translating language snippets, as these errors often go uncorrected or are only identified by automated systems.
Innovation Solution
A natural language correction system that generates correction models by identifying and filtering viable correction pairs from language snippet pairs, using edit distance algorithms to align words and extract rules for auto-correction or machine translation preprocessing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If machine translation engines translate language snippets containing errors, then translation speed is maintained, but translation accuracy deteriorates
Solution Approach 1:
The system performs preliminary error detection and correction on source language snippets before they are translated. By identifying and correcting errors in advance using training data and correction models, the system ensures that the translation engine receives clean input, thereby maintaining both translation speed and accuracy without requiring post-translation correction
Solution Approach 2:
The patent introduces an intermediary correction system between the source language input and the translation engine. This intermediary component processes the source text through error detection and correction models, acting as a mediator that cleans the input data before translation, thus resolving the conflict between speed and accuracy by preventing error propagation rather than dealing with it after translation
2Measurement precision
If automated error correction systems are implemented, then translation accuracy is improved, but system complexity increases
Solution Approach 1:
The correction system is trained on bilingual training data containing error examples and learns to automatically detect and correct errors without human intervention. The system serves itself by using the training data to build correction models that autonomously process source language snippets, reducing the need for complex manual correction workflows while maintaining high translation accuracy
Solution Approach 2:
The system performs preliminary error detection and correction on source language snippets before they are translated. By identifying and correcting errors in advance using training data and correction models, the system ensures that the translation engine receives clean input, thereby maintaining both translation speed and accuracy without requiring post-translation correction
Data Source
AI summary
Technology is disclosed for building correction models that correct natural language snippets. Correction models can include rules comprising pairs of word sequences identified from viable correction snippet pairs, where a first sequence of words in the pair should be replaced with a second sequence of words in the pair. Viable correction snippet pairs can be identified from among pairs of language snippets, such as a post to a social media website and a subsequent update to that post. Viable corrections can be the snippet pairs that both have no more unaligned words than a word alignment threshold and have no aligned word pair with a character edit difference above an edit distance threshold. In some implementations, word alignments can be found by aligning all the characters between a pair of language snippets, and identifying aligned words as those that have at least one aligned letter in common.