Seq2Seq Advanced Tutorial

This tutorial delves into the advanced concepts and techniques of Sequence-to-Sequence (Seq2Seq) models. Seq2Seq models are powerful tools for tasks like machine translation, summarization, and more. In this guide, we'll explore the intricacies of these models and how they can be improved.

Introduction

Seq2Seq models are based on the idea of encoding a sequence of inputs into a fixed-size vector and then decoding that vector into a sequence of outputs. This tutorial will cover the following topics:

The architecture of Seq2Seq models
The use of Encoder-Decoder structures
Attention mechanisms
Training and evaluation techniques

Architecture

Seq2Seq models typically consist of two main components: the encoder and the decoder. The encoder processes the input sequence and generates a fixed-size representation, often called the context vector. The decoder then uses this context vector to generate the output sequence.

Encoder

The encoder is usually based on a recurrent neural network (RNN) or a variant like Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU). These networks are capable of capturing the temporal dependencies in the input sequence.

Decoder

The decoder also uses an RNN to generate the output sequence. It takes the context vector from the encoder and the previous output token as inputs to predict the next token.

Attention Mechanisms

Attention mechanisms are a key component of Seq2Seq models, allowing the decoder to focus on different parts of the input sequence when generating each output token. This helps improve the model's ability to capture long-range dependencies.

Training and Evaluation

Training Seq2Seq models requires a large amount of parallel data, where the input and output sequences are aligned. Evaluation metrics like BLEU score are commonly used to measure the quality of translations produced by the model.