18249275. Dynamic Language Models for Continuously Evolving Content simplified abstract (Google LLC)
Dynamic Language Models for Continuously Evolving Content
Organization Name
Inventor(s)
Spurthi Amba Hombaiah of Mountain View CA (US)
Mingyang Zhang of San Jose CA (US)
Michael Bendersky of Cupertino CA (US)
Marc Alexander Najork of Palo Alto CA (US)
Dynamic Language Models for Continuously Evolving Content - A simplified explanation of the abstract
This abstract first appeared for US patent application 18249275 titled 'Dynamic Language Models for Continuously Evolving Content
Simplified Explanation
The patent application describes systems and methods for training machine learning models to adapt to changes in data distribution, specifically in the context of natural language models with a dynamically changing vocabulary. The innovation enables incremental training of the models, avoiding the need for retraining from scratch.
- The patent application focuses on incremental training of machine learning models to adapt to changes in vocabulary.
- The techniques described are particularly beneficial for natural language models.
- Incremental training allows models to adapt to evolving vocabulary without the need for retraining from scratch.
- The approach is feasible and cost-effective.
- The innovation provides a solution for adapting machine learning models to changes in underlying data distribution.
Potential Applications
The technology described in the patent application has potential applications in various fields, including:
- Natural language processing: Incremental training can be used to adapt language models to evolving vocabulary in real-time.
- Chatbots and virtual assistants: The models can be continuously trained to understand and respond to new words and phrases.
- Text classification: Incremental training can improve the accuracy of models in classifying documents with changing terminology.
- Sentiment analysis: The technology can be applied to sentiment analysis models to adapt to new words and expressions.
Problems Solved
The technology presented in the patent application addresses the following problems:
- Adapting machine learning models to changes in vocabulary without retraining from scratch.
- Keeping language models up-to-date with evolving language and terminology.
- Avoiding the need for manual intervention to update models with new words and phrases.
- Providing a cost-effective and efficient solution for training models to adapt to changes in data distribution.
Benefits
The described technology offers several benefits:
- Enables machine learning models to adapt to changes in vocabulary in real-time.
- Reduces the need for manual intervention and retraining of models.
- Cost-effective approach compared to retraining models from scratch.
- Improves the accuracy and performance of models by incorporating new words and expressions.
Original Abstract Submitted
Provided are systems and methods for incremental training of machine learning models to adapt to changes in an underlying data distribution. One example setting in which the techniques described herein may be beneficial is for incrementally training natural language models to enable the models to have or adapt to a dynamically changing vocabulary. Incremental training is provided as a feasible and inexpensive way of adapting machine learning models to evolving vocabulary without having to retrain them from scratch.
