Question 7Which transformer-based model architecture is well-suited to the task of text translation?1 pointSequence-to-sequenceAutoencoderAutoregressive

The transformer-based model architecture that is well-suited to the task of text translation is the Sequence-to-sequence model.

Here's a step-by-step explanation:

1. The Sequence-to-sequence (Seq2Seq) model is a type of transformer-based model architecture that is designed to convert sequences from one domain (e.g., sentences in English) to sequences in another domain (e.g., the same sentences translated into French).

2. It works by encoding the input sequence into a single vector, which can be thought of as an "abstract" representation of the sequence. This vector is then decoded into the output sequence. The encoding and decoding are done by two separate components of the model, known as the encoder and decoder.

3. The Seq2Seq model is particularly well-suited to tasks like text translation, where the input and output sequences can be of different lengths, and there is not a one-to-one correspondence between the elements of the input and output sequences.

4. In contrast, an autoencoder is a type of neural network used for learning efficient codings of input data, typically for the purpose of dimensionality reduction or denoising. Autoencoders are not typically used for text translation.

5. Autoregressive models, on the other hand, are used to predict future values based on past values in time series data. They are not typically used for text translation either.