What Sequence-to-Sequence Meaning, Applications & Example
Model architecture for transforming input sequences to output sequences.
What is Sequence-to-Sequence?
Sequence-to-Sequence (Seq2Seq) is a machine learning model architecture used for transforming one sequence into another. It’s commonly used in tasks like language translation, speech recognition, and text summarization, where the input and output are both sequences of data (e.g., a sentence in one language to a sentence in another language).
Components of Sequence-to-Sequence
- Encoder: Takes the input sequence and processes it into a fixed-size vector representation (often called the context vector).
- Decoder: Uses the context vector from the encoder to generate the output sequence, one element at a time.
Applications of Sequence-to-Sequence
- Machine Translation: Translates text from one language to another (e.g., English to French).
- Speech Recognition: Converts spoken language into text.
- Text Summarization: Generates a concise summary of a longer document.
- Question Answering: Provides an answer to a question based on context or a document.
Example of Sequence-to-Sequence
An example of a Sequence-to-Sequence model would be translating a sentence from English to French. The encoder processes the English sentence, and the decoder generates the French translation.
# Pseudo code example for Seq2Seq model
encoder_output, hidden_state = encoder(input_sequence)
output_sequence = decoder(encoder_output, hidden_state)