Encoder Decoder Architecture Explained for Machine Translation Seq2Seq NLP

In this video, we introduce the Encoder–Decoder architecture used in Natural Language Processing for sequence-to-sequence tasks such as machine translation. This architecture became one of the most important breakthroughs in deep learning for language tasks and laid the foundation for many modern NLP systems. Here is the GitHub repo link: https://github.com/switch2ai You can download all the code, scripts, and documents from the above GitHub repository. We start by understanding the machine translation problem. In machine translation, a sentence in the source language is converted into another language called the target language. For example, a sentence in English such as “Boy eats an apple” can be translated into Hindi as “Ladke ne seb khaya”. To solve this problem, sequence-to-sequence models are used. These models consist of two main components: an encoder and a decoder. The encoder processes the input sentence word by word and converts it into a fixed-length numerical representation known as the context vector. Initially the hidden state starts with a zero vector. As each word is processed, the hidden state is updated and begins capturing the meaning of the sentence. For example, the hidden state gradually builds context as “Boy”, “Boy eats”, “Boy eats an”, and finally “Boy eats an apple”. The final hidden state contains the complete representation of the input sentence and becomes the context vector. This context vector is then passed to the decoder. The decoder is responsible for generating the translated sentence one word at a time. The decoding process usually begins with a special token called Start of Sentence (SoS). Based on the context vector and previously generated words, the decoder predicts the next word in the target sequence until it reaches the End of Sentence (EoS) token. We also discuss an important training technique called teacher forcing. During training, the decoder normally uses the previously generated output as the next input. Howe