Sequence Generation Models

This chapter introduces sequence generation models, beginning with the foundational sequence-to-sequence (Seq2Seq) framework, which uses an encoder-decoder architecture for tasks like machine translation. It first explains the implementation of Seq2Seq models using Recurrent Neural Networks (RNNs) and discusses common decoding methods such as greedy decoding and beam search strategies. The text then introduces the attention mechanism as a method to improve model focus and efficiency. Subsequently, the chapter details the Transformer architecture, which leverages a self-attention mechanism to overcome the limitations of RNNs, such as difficulties with parallelization and long-distance dependencies. The core components of the Transformer, including its encoder and decoder structures, multi-head attention, and positional encoding, are explained. Finally, the chapter provides an overview of modern models derived from the Transformer’s structure, categorizing them into autoencoding (AE) models like BERT and autoregressive (AR) models like GPT.