Which component of the Transformer model is responsible for generating rich contextual representations of input text?

The Encoder component of the Transformer model is responsible for generating rich contextual representations of input text. It takes the input sequence and transforms it into a series of continuous representations that capture various aspects of the input data, effectively encoding the information in a way that is useful for downstream tasks.

The Encoder consists of multiple layers, each containing components such as self-attention mechanisms and feedforward neural networks. Throughout these layers, the Encoder processes the input in parallel, allowing it to consider the relationships between different tokens in the sequence. This leads to a more nuanced understanding of context, as it can learn dependencies irrespective of their distances in the input text.

The contextual representation produced by the Encoder can be thought of as a comprehensive summary of the input, encapsulating meaning, relations, and structure, which are essential for tasks such as translation, summarization, or sentiment analysis.