Learning Phrase Representations Using Encoder-Decoder

We’ve seen how RNNs and LSTMs process sequences by handling inputs one step at a time while maintaining context through a hidden state. This approach works well for tasks such as predicting the next word or classifying a sentence’s sentiment, but many real-world problems demand mapping an entire input sequence (like an English paragraph) to a complete output sequence (like a French translation). Simply predicting the next token or assigning a single label to the entire sequence isn’t enough.

For example, translating “I like cats” into “J’aime les chats” requires the model to read and fully understand the complete English sentence before generating the French sentence from start to finish. A classic RNN compresses all the information into its final hidden state. However, if the input sentence is more complex—say, “Yesterday, the brilliant musician who performed at the large concert hall was invited to play next summer”—a single hidden state may end up losing or mixing up key details, especially when important elements like “yesterday” appear at the beginning and crucial context like “next summer” appears at the end.

This challenge is known as the bottleneck problem: once the RNN has processed the entire sequence, it must condense all the information into one compressed vector before producing the output. If that vector fails to capture essential nuances—such as the timing of events or the specific roles of different subjects—the resulting translation or summary can become jumbled. Simply put, the bottleneck in sequence-to-sequence models is like cramming an entire novel into a single tweet! We can’t just do that!

What is the encoder-decoder framework?

In 2014, Kyunghyun Cho and colleagues, in their paper called “Learning Phrase Representations using RNN Encoder–Decoderfor Statistical Machine Translation”https://arxiv.org/pdf/1406.1078, proposed a breakthrough in handling sequence-to-sequence tasks: split the RNN into two distinct parts—an Encoder and a Decoder—so that each can specialize in its role. Think of it as assigning two complementary roles in a conversation:

• Encoder: A “listener” that carefully processes the entire source sentence, absorbing details and building an internal summary.