What is bidirectional RNN?

A bidirectional RNN is a recurrent neural network that processes a sequence in both directions: from beginning to end and from end to beginning.

This allows the model to use information from both earlier and later parts of the sequence when creating a representation for each position.

How it works

A bidirectional RNN usually contains two recurrent networks:

• Forward RNN: reads the sequence from left to right.
• Backward RNN: reads the sequence from right to left.

Their hidden states are then combined.

A simplified flow is:

Forward: x1 → x2 → x3 → x4

Backward: x1 ← x2 ← x3 ← x4

Combined representation = forward state + backward state

The combination may be done by concatenation or another merging operation.

Why is bidirectional processing useful?

For some tasks, understanding the current element depends on both what came before and what comes after it.

For example, in a sentence, the meaning of a word can depend on surrounding words on both sides.

Using both directions can therefore improve representations for tasks such as:

• Sequence labeling
• Named entity recognition
• Speech processing
• Text classification

Important limitation

A bidirectional RNN requires access to the complete sequence because the backward network needs future elements.

This makes it unsuitable for some real-time or strictly causal tasks where future inputs are not yet available.

For the basic recurrent architecture, see “What is a recurrent neural network (RNN)?”.

In short, a bidirectional RNN processes a sequence in both forward and backward directions so each position can use context from both the past and the future.