Learning Phrase Representations Using Encoder-Decoder

RNNs and LSTMs handle inputs step by step while keeping context hidden. This works for tasks like predicting the next word or classifying sentiment, but many real-world problems require mapping an entire sequence to another, such as translating an English paragraph into French.

For example, to translate “I like cats” into “J’aime les chats,” the model must understand the full sentence before producing the output. A basic RNN compresses all this information into its final hidden state. With longer sentences like “Yesterday, the brilliant musician who performed at the large concert hall was invited to play next summer,” details at the start and end often get lost or mixed up.

This is called the bottleneck problem. The model tries to squeeze an entire sequence into a single compressed vector, and critical nuances may vanish. It is like cramming an entire novel into a single tweet.

What is the encoder-decoder framework?

To overcome the bottleneck problem, researchers introduced the  ...