Multilingual Embedding Training via Machine Translation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional natural-language processing (NLP) techniques face challenges in scaling machine-learning models to process multiple languages due to the need for human annotation, which is costly and time-consuming, limiting their ability to support diverse languages and users.
Innovation Solution
The use of machine-assisted translation and multilingual embeddings to train machine-learning models, allowing for the generation of multilingual embeddings from source documents in one language to enable efficient training across different languages without manual annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human annotation is used to train machine-learning models for multiple languages, then the model accuracy and language understanding are improved, but the training cost and time consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training machine-learning models on large-scale multilingual datasets before fine-tuning them for specific NLP tasks. This pre-training phase establishes foundational language understanding across multiple languages, reducing the need for extensive human annotation during task-specific training. The model is prepared in advance with multilingual capabilities through techniques like multilingual word embeddings and cross-lingual transfer learning.
Solution Approach 2:
The patent uses copying by leveraging translations of annotated data from source languages to target languages. Instead of creating new annotations for each language, the system copies annotations from well-resourced languages and adapts them to lower-resource languages through machine translation and back-translation techniques, significantly reducing annotation effort while maintaining model accuracy.
2Measurement precision
If human annotation is used to train machine-learning models for multiple languages, then the model accuracy and language understanding are improved, but the training cost increases
Solution Approach 1:
The patent implements universality by developing a single multilingual machine-learning model that can perform multiple NLP tasks across different languages simultaneously. Instead of training separate monolingual models for each language, the system creates a universal model with multilingual capabilities that can be applied to various tasks such as sentiment analysis, named entity recognition, and text classification across many languages, reducing overall training and deployment costs.
Solution Approach 2:
The patent uses copying by reusing annotated training data from source languages for target languages through machine translation. Instead of paying for separate human annotation for each language, the system copies existing annotations and adapts them to new languages, significantly reducing annotation costs while maintaining acceptable model performance for lower-resource languages.
3Ease of manufacture
If conventional NLP techniques are used, then the implementation is straightforward for single languages, but the scalability to support diverse languages is limited
Solution Approach 1:
The patent applies segmentation by dividing the multilingual model training process into distinct modules: language-independent feature extraction, language-specific adaptation layers, and task-specific processing components. This modular architecture allows the system to maintain simplicity for single-language implementations while enabling easy extension to multiple languages by adding or configuring language modules without redesigning the entire system.
Solution Approach 2:
The patent uses another dimension by introducing a language dimension to the model architecture. Instead of creating separate models for each language, the system adds a language identifier dimension that allows the same model to process multiple languages by routing inputs through language-specific processing paths or applying language-adapted parameters, thereby achieving scalability while maintaining implementation simplicity.
Data Source
AI summary
Disclosed embodiments may provide techniques for training a machine-learning model using machine translation and multilingual embeddings. A computer-implemented method can include receiving a source document that includes text segments associated with a source language. In some instances, one or more of the text segments are associated with a target label. The computer-implemented method can also include translating the text of the source document to generate a set of translated documents that include text associated with a target language. The computer-implemented method can also include generating a set of labeled multilingual documents by mapping the target label of the source document to corresponding text segments of the set of translated documents. The computer-implemented method can also include encoding the text of the set of labeled multilingual documents into a plurality of multilingual embeddings. The computer-implemented method can also include training a machine-learning model using the plurality of multilingual embeddings.