Learning Phrase Representations Using Encoder-Decoder
Explore the encoder-decoder framework for handling sequence-to-sequence tasks like language translation. Understand how this approach overcomes the bottleneck problem by separating input encoding and output decoding, allowing models to learn meaningful phrase representations. Gain insights into improvements with LSTMs and techniques that paved the way for modern attention mechanisms, enhancing language model performance.
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. ...