Reading Comprehension Model Training via Reference Output Difference
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Solution Overview
Problem
Existing reading comprehension models struggle to effectively learn knowledge from multiple fields, leading to suboptimal performance in multi-field data processing.
Innovation Solution
A method for training a reading comprehension model by obtaining a target model and a reference model, inputting training samples to both models, and performing a training process based on the difference between their output data to adjust the target model's parameters, allowing it to absorb data from the reference field and improve its performance across multiple fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a reading comprehension model is trained using training data from multiple fields, then the model can learn knowledge from multiple fields, but the model performance deteriorates due to inability to effectively process multi-field data
Solution Approach 1:
The patent introduces a reference reading comprehension model as an intermediary to transfer knowledge from the reference field to the target field. The reference model processes training samples to generate output data that serves as guidance for training the target model, enabling effective multi-field knowledge transfer without direct mixing of field-specific data
Solution Approach 2:
The patent changes the training parameters by using the difference between output data from the reference model and target model as the training objective. This parameter transformation allows the target model to learn from the reference model's processing of multi-field data while maintaining field-specific performance characteristics
2Adaptability or versatility
If training data from multiple fields is used to train the reading comprehension model, then the model can handle diverse data, but the processing effectiveness decreases
Solution Approach 1:
The reference reading comprehension model acts as an intermediary that pre-processes multi-field training samples, extracting generalized patterns that can be transferred to the target model. This intermediary processing step enables the target model to learn from diverse data without being overwhelmed by field-specific variations
Solution Approach 2:
The patent copies the processing capability of the reference model to the target model through knowledge transfer. By using the reference model's output on multi-field data as training signals, the target model acquires the ability to process diverse data effectively without directly training on all multi-field data
Data Source
AI summary
The present disclosure discloses a method for training a reading comprehension model, and relates to a field of natural language processing and deep learning technologies. The detailed implementing solution includes: respectively inputting a first training sample of the reference field into a reference reading comprehension model of a reference field and a target reading comprehension model of a target field, to obtain first output data output by the reference reading comprehension model and second output data output by the target reading comprehension model; and performing a first training process on the target reading comprehension model based on a difference between the first output data and the second output data.