Multi-Task Text Encoder Training for NLP Efficiency
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Solution Overview
Problem
Existing natural language processing solutions face inefficiencies in training and resource utilization due to the need for large datasets and computational resources, particularly in generating semantically rich inputs for downstream models.
Innovation Solution
An attention-based text encoder machine learning model is trained using a multi-task training routine that combines language modeling, similarity determination, and document classification tasks to generate word-wise and document-wide embedded representations, optimizing parameter values through sequential and concurrent learning loss models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional single-task training is used for NLP models, then the model can be trained with a clear objective, but it requires large datasets and extensive computational resources
Solution Approach 1:
The patent applies multi-task training where a single NLP model is trained to perform multiple tasks simultaneously (language modeling, similarity determination, and document classification). This multi-functionality allows the model to learn diverse patterns from the training data, improving its effectiveness and reliability while reducing the need for separate large-scale training datasets for each individual task.
2Reliability
If traditional single-task training is used for NLP models, then the training objective is clear, but computational resources and training time are excessive
Solution Approach 1:
By training the model to perform multiple tasks simultaneously through a unified loss function that combines language modeling loss, similarity determination loss, and document classification loss, the patent reduces the number of separate training iterations needed. The model learns transferable representations that generalize across tasks, improving training effectiveness while reducing time loss.
Solution Approach 2:
The patent merges multiple training objectives into a single multi-task training framework. By combining language modeling, similarity determination, and document classification tasks into one unified training process with a composite loss function, the model achieves better performance with fewer training iterations compared to sequential or separate training approaches.
3Measurement precision
If the model generates semantically rich inputs for downstream models, then the quality of downstream processing improves, but storage and computational resources increase
Solution Approach 1:
The patent generates word-wise embedded representations that provide localized semantic information for each word in the document, rather than creating comprehensive but resource-intensive full-document representations. This local quality approach maintains high semantic representation quality for downstream tasks while reducing overall storage requirements by only computing embeddings where needed.
4Measurement precision
If comprehensive document representations are generated, then downstream model accuracy improves, but resource utilization in post-prediction systems deteriorates
Solution Approach 1:
The multi-task trained model produces document-wide embedded representations that serve multiple downstream purposes simultaneously. These comprehensive representations improve downstream model accuracy while the model's efficiency in generating them (through optimized parameter values from multi-task training) maintains good resource utilization in post-prediction systems.
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing operations using an attention-based text encoder machine learning model that is trained using a multi-task training routine that is associated with two or more training tasks (e.g., a multi-task training routine that is associated with two or more sequential training tasks, a multi-training routine that is associated with two or more concurrent training tasks, and/or the like).