Understanding Sequence-to-Sequence (Seq2Seq) Models and their Significance
Introduction
In recent years, the field of natural language processing (NLP) has witnessed remarkable advancements, and one of the prominent breakthroughs is the development of Sequence-to-Sequence (Seq2Seq) models. Seq2Seq models have revolutionized various NLP tasks by enabling the transformation of sequences from one domain to another, offering solutions to machine translation, text summarization, speech recognition, and more. This essay aims to delve into the intricacies of Seq2Seq models, exploring their architecture, training process, applications, and significance in the realm of NLP.
Architecture of Seq2Seq Models
At its core, a Seq2Seq model consists of two main components: an encoder and a decoder. These components work together to process input sequences and generate corresponding output sequences. The encoder takes the input sequence and compresses it into a fixed-size vector, often called the context vector or the thought vector. This vector contains the salient information from the input sequence and serves as the initial state for the decoder.
The decoder then generates the output sequence by predicting one token at a time. It takes the context vector as its initial state and employs techniques likeβ¦
