Sequence-to-sequence learning (Seq2Seq) is a type of machine learning where models are trained to transform one sequence of items (words, letters, features, etc.) into another sequence.
Understanding Sequence-to-Sequence Learning
Seq2Seq models are particularly useful when the input and output are both sequences and may have different lengths. This contrasts with other machine learning tasks where the input is a fixed-size vector, and the output is a single class label or value.
Key Concepts
- Input Sequence: The sequence that is fed into the model.
- Output Sequence: The sequence that the model is expected to produce.
- Encoder: Part of the model that processes the input sequence and encodes it into a fixed-length vector, often called the "context vector" or "thought vector." This vector is meant to represent the meaning of the input sequence.
- Decoder: Part of the model that takes the context vector from the encoder and generates the output sequence.
How it Works
- Encoding: The encoder reads the input sequence step by step and transforms it into a context vector.
- Decoding: The decoder uses the context vector to generate the output sequence, one element at a time.
Example
According to the provided reference, Seq2Seq models are used to "convert sequences from one domain (e.g. sentences in English) to sequences in another domain (e.g. the same sentences translated to French)."
For example:
| Input (English) | Seq2Seq Model | Output (French) |
|---|---|---|
| "the cat sat on the mat" | [Seq2Seq model processing] | "le chat etait assis sur le tapis" |
Applications
Seq2Seq learning has broad applications:
- Machine Translation: Translating text from one language to another.
- Speech Recognition: Converting audio sequences into text.
- Text Summarization: Generating a shorter summary from a longer text.
- Chatbots: Generating conversational responses.
- Code Generation: Translating a natural language description into executable code.
