Bidirectional Long Short-Term Memory (BiLSTM or BLSTM) is a deep learning architecture for sequence-to-sequence tasks that provides an effective way to process sequences of data. In order to process variable-length sequences of input and output in a supervised learning environment, BiLSTM architectures combine the forward-looking recurrent neural network (RNN) architecture with a backward-looking RNN architecture in a bidirectional fashion. This capability enables BiLSTM to capture temporal information more accurately and effectively than conventional recurrent neural networks, leading to improved performance in many tasks such as speech recognition, natural language processing (NLP), time series forecasting, and more.
At its core, a BiLSTM consists of two separate RNNs: a forward-looking RNN, and a backward-looking RNN. The forward-looking RNN processes input from left to right, while the backward-looking RNN processes input from right to left. The output from each of these networks is then combined into a single representation, which can then be used as input to a fully-connected, or dense, output layer.
The advantage of combining two RNNs into a single architecture lies in its ability to capture long-term temporal dynamics. By looping information over long sequences of data in both directions (from left to right and from right to left), BiLSTM architectures are able to capture not only the direct interactions between elements but also how elements may interact with each other over extended periods of time. This could give BiLSTMs an advantage over other sequence-based architectures that may be limited to capturing only immediate or short-term temporal effects.
BiLSTMs have proven to be effective in a number of tasks, including character-level language modeling, handwriting recognition, and sentiment analysis. They have become increasingly popular in recent years due to their flexibility and ability to capture temporal patterns in data. As a result, they have become a go-to architecture for many tasks related to sequence-to-sequence learning.
