Overview of Universal Language Model Fine-Tuning (ULMFiT)
Universal Language Model Fine-tuning, or ULMFiT, is a technique for natural language processing (NLP) tasks. It uses a 3-layer architecture called AWD-LSTM for creating representations of text, which involves pre-training the model on Wikipedia-based text, fine-tuning it on a target task, and fine-tuning the classifier on that task.
Architecture and Training
The AWD-LSTM architecture is a neural network consisting of three layers, each of which captures different types of information. The training process involves pre-training the model on a large corpus of text, such as Wikipedia-based text, before fine-tuning it on a specific NLP task. This is followed by fine-tuning the classifier on that task.
During the training process, each layer is fine-tuned to different extents using the discriminative fine-tuning technique, which ensures that layers capture specific features of the data. ULMFiT uses the slanted triangular learning rates strategy to schedule learning rates, which first linearly increases the learning rate and then linearly decays it.
Gradual Unfreezing
The ULMFiT technique uses gradual unfreezing to fine-tune the target classifier. Gradual unfreezing starts with the last layer, which contains the least general knowledge, and gradually unfreezes the other layers one at a time. By doing this, the model learns specific features for the target task without forgetting general knowledge learned during pre-training.
During fine-tuning, each group of unfrozen layers is fine-tuned for one epoch before moving onto the next group. This process is repeated until all layers are fine-tuned until convergence at the last iteration.
Applications of ULMFiT
The ULMFiT method has been successfully applied to various NLP tasks such as sentiment analysis, text classification, and machine translation. By pre-training on a large corpus of data, the technique can learn general language features which can then be fine-tuned for specific tasks.
ULMFiT has also shown to be effective when dealing with limited amounts of data, as the pre-training on a large corpus enables the model to learn general representations of text that can be applied to small datasets. Additionally, ULMFiT can be adapted for specific domains or languages by retraining the model on domain-specific or language-specific data.
ULMFiT is an effective transfer learning technique for NLP tasks that uses pre-training on a large corpus of data and gradual unfreezing to fine-tune the model for specific tasks. The technique has shown remarkable results when dealing with limited amounts of data and can be adapted for specific domains or languages. Its success at various NLP tasks has encouraged further research on transfer learning techniques in the field.
