In Transformer-based translation models like T5 or MarianMT:
Role of the Decoder:
- The decoder's primary function is to generate the target language output sequence (translated text) one token at a time
- It uses the encoded context from the encoder to predict the next word in the output sequence
- The decoder attends to both the encoder's output and its own previous outputs through self-attention and encoder-decoder attention mechanisms
Why other options are incorrect:
- A: Encoding the input sentence into embeddings is the role of the encoder, not the decoder
- C: Removing noise from text is not a function of the decoder in translation models
- D: Tokenization is a preprocessing step performed before the input reaches the model architecture
Key Points:
- Encoder processes the source language input and creates contextual representations
- Decoder uses these representations to generate the target language output
- The decoder operates autoregressively, predicting each token based on previous tokens and encoder context
