Answer: Predicts the next word using encoded context

In Transformer-based translation models like T5 or MarianMT, the decoder's primary role is to generate the output sequence (translated text) by predicting the next word using the encoded context from the encoder. The encoder processes the input sentence and creates contextual embeddings, while the decoder uses these embeddings along with previously generated tokens to predict subsequent tokens in the output sequence. This autoregressive generation process is fundamental to sequence-to-sequence tasks like machine translation.

Author: Jin H