Transfer learning is a powerful technique that allows machine learning models to apply knowledge learned in one setting to new, related tasks. This enables more efficient model development, especially for natural language processing (NLP) applications with limited training data.
In NLP, labelled datasets required for supervised learning are often small and expensive to obtain at scale. By utilizing transfer learning instead of training models from scratch, high performance can be unlocked with less data.
This article provides practical guidelines for implementing different transfer learning approaches to boost NLP model effectiveness across use cases like sentiment analysis, question answering, named entity recognition and more.
Transfer Learning Methods for NLP
There are three primary ways to implement transfer learning for NLP:
- Feature Extraction - The outputs of an already trained network are used as input features for a new model. For example, Word2Vec or BERT embeddings can initialize an LSTM for text classification. This strategy enables swift model development.
- Fine-Tuning - An existing pre-trained model is adapted through continued training on domain-specific data. For instance, a BERT model pre-trained on Wikipedia can be fine-tuned with legal case documents. Fine-tuning strikes a balance between customization and computational efficiency.
- Multi-Task Learning - A single model is trained concurrently on several related tasks, sharing representations between them. The shared knowledge benefits all tasks. For example, joint training on POS, parsing and NER tagging.
Best Practices for Fine-Tuning
As fine-tuning often produces state-of-the-art results for NLP, here are some recommendations:
- Choosing Model Architecture - Leverage SOTA models like BERT and T5. Balance task suitability and computational constraints.
- Unfreezing Layers - Unfreeze at least the final classifier layer first. Then unfreeze encoder blocks sequentially for increased customization.
- Learning Rates - Use slanted triangular learning rates and discriminate fine-tuning to stabilize optimization.
- Regularization Techniques - Apply dropout, early stopping, weight decay and ensembling to prevent overfitting.
Case Studies
Sentiment Analysis
Fine-tuning BERT achieved over 97% accuracy on movie review sentiment, outperforming RNN and CNN baselines.
Question Answering
T5 fine-tuned on domain-specific QA data matched expert human performance on medical and scientific QA.
Named Entity Recognition
A BERT-BiLSTM-CRF pipeline, fine-tuned on news and social media named entities, surpassed previous benchmarks by 3 F1 points.
In summary, judicious application of transfer learning substantially pushes forward the state-of-the-art in NLP, enabling models that match or exceed human-level language understanding with relatively little data. This pathway continues to offer immense potential as foundation models are refined and adapted to more specialized domains. By following best practices around transfer learning approaches, the efficiency and performance of natural language models can be significantly enhanced.
