Learn what a sequence-to-sequence model is and how it can use the transformer architecture to perform text translation, a task of transforming a text from one natural language to another.
Which transformer-based model architecture is well-suited to the task of text translation?
The correct answer is A. Sequence-to-sequence. A sequence-to-sequence model is a type of transformer-based model architecture that is well-suited to the task of text translation. Text translation is the task of transforming a text from one natural language to another, such as from English to French or from Chinese to Spanish.
A sequence-to-sequence model consists of two parts: an encoder and a decoder. The encoder takes the input text in the source language and encodes it into a latent representation, which captures the meaning and structure of the text. The decoder then takes the latent representation and generates the output text in the target language, word by word, while attending to the encoder output and the previous decoder output.
A sequence-to-sequence model can leverage the transformer architecture, which uses self-attention mechanisms to capture the dependencies between words in a sequence. Self-attention allows the model to focus on the relevant parts of the input and output sequences, and to generate more fluent and accurate translations. An example of a sequence-to-sequence transformer model is T5, which can perform various natural language tasks, including translation, by using a text-to-text approach.
A sequence-to-sequence model differs from an autoencoder model, which reconstructs the input sequence by predicting masked tokens, such as BERT, or an autoregressive model, which predicts the next token in the sequence based on the previous tokens, such as GPT. An autoencoder model does not generate a new sequence, but rather fills in the gaps in the existing sequence. An autoregressive model does not use the input sequence as a guide, but rather generates a new sequence from scratch.
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