Understanding the Seq2Seq model
Following our exploration of autoencoders, another groundbreaking architecture in the realm of advanced sequential models is the Seq2Seq model. Central to many state-of-the-art natural language processing tasks, the Seq2Seq model exhibits a unique capability: transforming an input sequence into an output sequence that may differ in length. This flexibility allows it to excel in challenges like machine translation, where the source and target sentences can naturally differ in size.
Refer to Figure 11.3, which visualizes the core components of a Seq2Seq model:
Figure 11.3: Illustration of the Seq2Seq model architecture
Broadly, there are three main elements:
- Encoder: Processes the input sequence
- Thought vector: A bridge between the encoder and decoder
- Decoder: Generates the output sequence
Let us explore them one by one.
Encoder
The encoder is shown as Figure 11.3. As we can observe, it is an input...
