From RNNs to BERT: A Review of Neural Models for Sequence Learning

Learning sequence data is important in machine learning fields, including speech recognition, natural language processing, and time series prediction. Various approaches have been put out in recent years to manage these jobs. Early models like the Recurrent Neural Network (RNN) were able to process sequential information but encountered vanishing and exploding gradients problems. These issues were eventually addressed with the introduction of the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU), which enhanced the capacity to learn long-term dependencies. The proposal of the attention mechanisms further enhanced the GRUs performance and led the Transformer model to replace recurrence with attention, making training faster and more effective for large-scale data. Furthermore, BERT used pre-training and fine-tuning methods that brought a remarkable improvement in many NLP tasks. This paper reviews the development of these models, introduces the mechanisms of each model, compares their strengths and weaknesses, and finally discusses the challenges that still remain.