Transformer Language Model Inter-Training via Partitioning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Transformer-based language models face performance issues when fine-tuned with limited labeled data, especially in text classification tasks where data scarcity and labeling challenges hinder optimal performance.
Innovation Solution
The system pre-trains a transformer-based language model on a general domain and inter-trains it using partitioning and classification techniques, generating an inter-trained model that is then fine-tuned on a target task without additional labeled data, leveraging clustering to create pseudo-labels for unsupervised inter-training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If transformer-based language models are fine-tuned with limited labeled data, then the model can be adapted to specific target tasks, but classification performance deteriorates due to data scarcity
Solution Approach 1:
The patent applies preliminary action by performing unsupervised inter-training on unlabeled domain-specific data before fine-tuning on labeled target task data. This preliminary domain adaptation step prepares the model by learning domain-specific language patterns and terminology without requiring labeled data, thereby improving subsequent fine-tuning performance when labeled data is scarce. The model is pre-adapted to the domain's linguistic characteristics before encountering the limited labeled examples.
Solution Approach 2:
The patent introduces an intermediary unsupervised domain adaptation phase between pre-training on general domain data and fine-tuning on labeled target task data. This intermediary step uses unlabeled domain-specific data as a bridge, allowing the model to gradually adapt to the target domain's language style and vocabulary without the need for labeled examples, thus smoothing the transition and improving final classification performance.
2Reliability
If more labeled data is collected for fine-tuning, then classification performance improves, but data labeling cost and time increase
Solution Approach 1:
The patent applies self-service by enabling the model to learn from unlabeled domain-specific data without human annotation. The unsupervised inter-training phase allows the model to autonomously adapt to domain-specific language patterns, terminology, and stylistic features using only unlabeled data, eliminating the need for time-consuming manual labeling while still achieving domain adaptation.
Solution Approach 2:
By performing unsupervised domain adaptation as a preliminary step before fine-tuning, the patent reduces the amount of labeled data needed for achieving good classification performance. The model预先 learns domain-specific characteristics from unlabeled data, so that subsequent fine-tuning with minimal labeled data can achieve better results, thereby reducing overall labeling time and resources required.
3Adaptability or versatility
If traditional pre-training on general domain data is used, then the model learns general language patterns, but performance on domain-specific tasks deteriorates due to lack of domain knowledge
Solution Approach 1:
The patent applies preliminary action by inserting an unsupervised domain adaptation phase between general domain pre-training and target task fine-tuning. During this intermediate phase, the model learns domain-specific language patterns, terminology, and stylistic features from unlabeled domain data, thereby acquiring domain knowledge before encountering labeled target task examples. This preliminary domain adaptation bridges the gap between general language capability and domain-specific performance.
Solution Approach 2:
The training process is segmented into three distinct phases: (1) pre-training on general domain data to learn fundamental language patterns, (2) unsupervised inter-training on unlabeled domain-specific data to acquire domain knowledge, and (3) fine-tuning on labeled target task data to achieve task-specific performance. This segmentation allows each phase to focus on specific learning objectives, progressively building from general language capability to domain-specific expertise.
Data Source
AI summary
An example system includes a processor to pre-train a transformer-based language model on a general domain. The processor can inter-train the pre-trained transformer-based language model using partitioning and classification to generate an inter-trained transformer-based pre-trained language model. The processor can then fine-tune the inter-trained transformer-based pre-trained language model on a target task to generate a fine-tuned transformer-based language model.