Learning Phrase Representations Using Encoder-Decoder
Explore how the encoder-decoder framework addresses the bottleneck problem in sequence tasks by using RNNs and LSTMs to learn phrase-level representations. Understand the roles of the encoder and decoder, and discover how advancements like reversed input and context vectors improve translation and sequence generation. This lesson lays the foundation for modern sequence-to-sequence modeling and sets up the transition to attention mechanisms.
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, such as “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 attempts to condense an entire sequence into a single compressed vector, which may cause important nuances to disappear. It is like cramming an entire novel into a single tweet.
What is the encoder-decoder framework?
To overcome the bottleneck problem, researchers introduced ...