Encoder and Decoder
In the context of deep learning and sequence-to-sequence tasks, an encoder-decoder architecture is a common framework used for tasks such as machine translation, text summarization, and speech recognition. The encoder-decoder model consists of two main components: an encoder and a decoder.
Encoder: The encoder is responsible for processing the input sequence and capturing its contextual information. It typically consists of multiple layers of a neural network (often based on the transformer architecture) that sequentially process the input tokens. The encoder reads the input sequence and generates a fixed-dimensional representation, often referred to as the "context vector" or "thought vector." This context vector summarizes the input sequence and contains the essential information for the subsequent decoding process.
Decoder: The decoder takes the context vector produced by the encoder and generates the output sequence token by token. Like the encoder, the decoder typically consists of multiple layers of a neural network. At each step, the decoder takes the previously generated token and the context vector as inputs and predicts the next token in the output sequence. This process is performed recursively until the entire output sequence is generated.
The encoder and decoder components work together to translate or transform the input sequence into the desired output sequence. The encoder processes the input sequence and encodes its information into a fixed-dimensional representation. The decoder then uses this representation to generate the output sequence, step by step.
During training, the model is provided with pairs of input sequences and their corresponding target output sequences. The encoder-decoder model is trained to minimize the discrepancy between the predicted output sequence and the target sequence. This is typically done using techniques such as teacher forcing, where the previously generated token (or ground truth token) is fed as input to the decoder at each step during training.
The encoder-decoder architecture is highly flexible and can be adapted to various sequence-to-sequence tasks. For example, in machine translation, the input sequence is a sentence in the source language, and the output sequence is the translation in the target language. In text summarization, the input sequence is a longer document, and the output sequence is a concise summary.
Overall, the encoder-decoder architecture enables the model to learn to map input sequences to output sequences by leveraging the contextual information captured in the encoder and the generation capabilities of the decoder