An Encoder-Decoder Architecture is a neural network design used for tasks where both input and output are sequences, such as translating sentences or summarizing text. It consists of two main parts:
- Encoder: Processes the input sequence and compresses it into a fixed-size context vector.
- Decoder: Uses this context vector to generate the output sequence.
Applications/Use Cases:
- Machine Translation: Converting text from one language to another.
- Text Summarization: Creating concise summaries of longer documents.
- Speech Recognition: Transcribing spoken words into text.