For a task where the model needs to understand context from both before and after a point in a sequence, what special kind of LSTM is used?

For a task requiring context understanding from both preceding and succeeding parts of a sequence, a special kind of LSTM called a Bidirectional Long Short-Term Memory (BiLSTM) network is used. A BiLSTM is constructed by combining two independent LSTM layers. One LSTM layer processes the input sequence in the forward direction, capturing context from past information up to the current point. The other LSTM layer processes the same input sequence in the reverse direction, capturing context from future information relative to the current point. At each time step in the sequence, the hidden state outputs from both the forward and backward LSTM layers are typically concatenated, meaning they are joined together to form a single, richer representation. This combined representation then encompasses information from both directions of the sequence. For example, in a sentence like "The bank will close at 5 PM," to understand if "bank" refers to a financial institution or a river's edge, a BiLSTM can utilize the context of "close at 5 PM" (future context) and "The" (past context) simultaneously. This bidirectional processing provides a complete contextual understanding, which is crucial for tasks like named entity recognition, machine translation, or sentiment analysis, where the meaning of a word or element often depends on its surrounding words.