AI Apparatus Translating Language Corpora for NLU Model Training
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Solution Overview
Problem
Building a natural language understanding model for a second language requires manual learning and collection of separate corpora for each language, which is inefficient and labor-intensive, especially for countries with different languages.
Innovation Solution
An artificial intelligence apparatus that uses a machine translation model to input a first language corpus and obtain a second language corpus, allowing the learning of a natural language understanding model by using the obtained second language corpus and corresponding commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual learning is used to build a natural language understanding model for each language, then the model can be accurately trained for that specific language, but the process becomes labor-intensive and inefficient
Solution Approach 1:
The patent applies the copying principle by using a pre-built natural language understanding model in a first language as a template to generate a corresponding model for a second language. Instead of manually collecting and labeling corpus data for each language, the system translates the first language corpus to the second language and uses the translated corpus along with the original model structure to efficiently train the second language model, significantly reducing manual effort while maintaining training accuracy
Solution Approach 2:
The patent applies preliminary action by pre-training the natural language understanding model on a first language corpus before adapting it to the second language. The model is first trained on available first language data, then this pre-trained model serves as the foundation for generating the second language model through corpus translation and fine-tuning, eliminating the need to start from scratch for each language
2Reliability
If separate corpora are collected for each language, then the model can be language-specific and accurate, but the process becomes complex and resource-intensive
Solution Approach 1:
The patent applies universality by creating a multi-functional system where a single natural language understanding model can be adapted to multiple languages through the corpus translation mechanism. The same model architecture and training process can handle different languages by simply translating the corpus, making the system universally applicable across languages without requiring separate development pipelines for each language
Solution Approach 2:
The patent uses translation as an intermediary mechanism to bridge between the first language corpus and the second language training data. Instead of manually creating second language corpus, the system translates the first language corpus into the second language, using this translated corpus as an intermediate step to generate the final training data, thereby simplifying the corpus collection process while maintaining language-specific accuracy
Data Source
AI summary
The present disclosure provides an artificial intelligence apparatus which inputs first language data into a machine translation model to economically train a natural language understanding model of a second language and obtains second language data corresponding to the first language data to train the natural language understanding model.