How do encoder-decoder models function overall?

Encoder-decoder models are an architecture commonly used in machine learning, particularly for tasks such as machine translation, summarization, and more. The primary function of these models is to transform input data into a format that can be easily worked on by the machine learning system and then to generate output data based on that format.

In this structure, the encoder takes the input sequences and processes them to produce a fixed-size vector that encapsulates the important information from the inputs. This vector acts as a compressed representation of the input data, capturing the essential features needed for understanding the context.

The decoder then uses this vector to generate the output sequences. It translates the encoded information back into a relevant format, often one that mirrors the structure of the input but in a different context, such as converting a sentence in one language to another. This process allows the model to learn the relationship between input and output sequences efficiently.

The other choices do not capture the full functionality of the encoder-decoder architecture. One choice inaccurately states the roles of the encoder and decoder and suggests independence between them, which does not reflect their designed interdependent operational flow. Overall, choice C correctly describes the transformational process at the heart of encoder-decoder models.