Unified NLP Model Joint Semantic Understanding and Generation
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Solution Overview
Problem
Current natural language processing models lack the ability to perform both semantic understanding and language generation simultaneously, requiring separate models and increasing resource and time consumption.
Innovation Solution
A unified pre-trained language model is trained using a collaborative framework that includes a unified training layer and an independent training layer, allowing for joint training of multiple tasks while enabling independent training for specific tasks, thereby improving the model's performance on both semantic understanding and language generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate models are used for semantic understanding and language generation, then each model can be optimized for its specific task, but resource consumption and time consumption increase
Solution Approach 1:
The patent merges semantic understanding and language generation into a single unified pre-trained language model. The model incorporates multiple task modules (semantic understanding module and language generation module) that share the same pre-trained language model backbone, enabling joint training and inference. This consolidation reduces training time and resource consumption while maintaining task-specific performance through modular task adapters.
Solution Approach 2:
The unified pre-trained language model is designed to perform multiple functions simultaneously. It serves as a universal foundation that can be adapted to both semantic understanding tasks (such as sentiment analysis, text classification) and language generation tasks (such as text summarization, question answering) through task-specific modules, eliminating the need for separate specialized models.
2Reliability
If separate models are used for semantic understanding and language generation, then each model can be optimized for its specific task, but resource consumption increases
Solution Approach 1:
The patent merges semantic understanding and language generation into a single unified pre-trained language model. The model incorporates multiple task modules (semantic understanding module and language generation module) that share the same pre-trained language model backbone, enabling joint training and inference. This consolidation reduces training time and resource consumption while maintaining task-specific performance through modular task adapters.
Solution Approach 2:
The unified pre-trained language model is designed to perform multiple functions simultaneously. It serves as a universal foundation that can be adapted to both semantic understanding tasks (such as sentiment analysis, text classification) and language generation tasks (such as text summarization, question answering) through task-specific modules, eliminating the need for separate specialized models.
3Productivity
If a unified model performs both semantic understanding and language generation, then resource and time consumption are reduced, but model complexity increases
Solution Approach 1:
The unified model is segmented into distinct functional modules: a shared pre-trained language model backbone and task-specific modules for semantic understanding and language generation. This segmentation allows the complex model to be organized into manageable components, where the backbone provides general language capabilities and task-specific modules handle particular functions, reducing the perceived complexity through modular architecture.
Solution Approach 2:
The unified pre-trained language model is designed to perform multiple functions simultaneously. It serves as a universal foundation that can be adapted to both semantic understanding tasks (such as sentiment analysis, text classification) and language generation tasks (such as text summarization, question answering) through task-specific modules, eliminating the need for separate specialized models.
Data Source
AI summary
The present application provides a method of training a natural language processing model, which relates to a field of artificial intelligence, and in particular to a field of natural language processing. A specific implementation scheme includes: performing a semantic learning for multi-tasks on an input text, so as to obtain a semantic feature for the multi-tasks, wherein the multi-tasks include a plurality of branch tasks; performing a feature learning for each branch task based on the semantic feature, so as to obtain a first output result for each branch task; calculating a loss for each branch task according to the first output result for the branch task; and adjusting a parameter of the natural language processing model according to the loss for each branch task. The present application further provides a method of processing a natural language, an electronic device, and a storage medium.