Multilingual Text Classification via Synthetic Data Translation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Supervised machine learning models for text analysis require large labeled training datasets, which can be laborious and costly to create, especially when dealing with multiple languages, posing a logistical hurdle for organizations with diverse linguistic needs.
Innovation Solution
Adapting text-analyzing machine learning classification models to process input in multiple languages without retraining by identifying influential token combinations and using similarity analysis techniques, such as embedding vectors or machine translation, to generate substitute text collections that can be classified using a single trained model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised machine learning models are trained on large labeled training datasets for each language, then classification accuracy is improved, but the time and cost for data preparation increases significantly
Solution Approach 1:
The patent creates synthetic labeled training data by copying and adapting existing labeled data from one language to multiple target languages using machine translation. Instead of collecting and labeling separate datasets for each language, the system generates synthetic training examples by translating source language data, thereby reducing data preparation time while maintaining classification accuracy through the use of translation-quality control mechanisms
Solution Approach 2:
The patent performs preliminary machine translation of training data before the actual model training process. By pre-translating source language training examples into target languages and preparing synthetic labeled datasets in advance, the system eliminates the need for time-consuming manual labeling during the training phase, thus reducing overall data preparation time while ensuring quality through pre-validation steps
2Measurement precision
If supervised machine learning models are trained on large labeled training datasets for each language, then prediction quality is improved, but the cost of data labeling increases significantly
Solution Approach 1:
The patent uses synthetic data generation by copying and translating existing labeled examples instead of creating new labeled datasets for each language. This approach dramatically reduces labeling costs by leveraging existing labeled data and automated translation processes, while maintaining prediction quality through careful translation quality control and synthetic data validation mechanisms
Solution Approach 2:
The system enables self-service data generation by automatically translating source language training examples into target languages without requiring manual intervention from subject matter experts for each language. The automated pipeline performs translation, synthetic data creation, and basic validation, significantly reducing the need for expensive human labeling resources while maintaining data quality
3Measurement precision
If separate machine learning models are trained for each language, then language-specific accuracy is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal machine learning model that can process multiple languages through a single trained model. By using synthetic multilingual training data generated through translation, the model learns language-agnostic patterns and can accurately classify text in multiple languages without requiring separate models for each language, thereby reducing system complexity while maintaining language-specific accuracy
Solution Approach 2:
The patent merges multiple language-specific training tasks into a single unified training process. By combining translated training examples from multiple languages into one dataset and training a single model, the system eliminates the need to manage multiple separate models, reducing complexity in model deployment, maintenance, and updates while preserving the ability to accurately process each language
4Measurement precision
If extensive labeled training data is collected for each language, then model performance is improved, but logistical complexity increases
Solution Approach 1:
The patent eliminates the need for separate data collection efforts for each language by copying and translating existing labeled training data. This approach simplifies logistics by using a single source dataset that can be adapted to multiple languages through automated translation, removing the complexity of coordinating separate data collection, validation, and labeling processes for each language while maintaining model performance
Data Source
AI summary
Influential token combinations corresponding to various classes associated with a machine learning model trained to classify text collections expressed in a first language are identified. A similarity metric between one or more influential token combinations and a classification candidate text collection (expressed in a second language) is determined. A substitute text collection corresponding to the classification candidate is generated based at least partly on the similarity metric. The substitute text collection is used as input to one or more machine learning models to identify a predicted class of the classification candidate.