Sequence-to-Sequence
Sequence-to-Sequence is a fundamental concept in Machine Learning and Natural Language Processing that involves the use of Neural Networks to generate output sequences from input sequences. This technique has been widely used in applications such as Chatbots, Machine Translation, and Text Summarization. The Sequence-to-Sequence model typically consists of an Encoder and a Decoder, where the encoder processes the input sequence and the decoder generates the output sequence. Ashish Vaswani and his colleagues introduced the Transformer model, which is a type of Sequence-to-Sequence model that relies entirely on Self-Attention mechanisms. This approach has achieved state-of-the-art results in many Natural Language Processing tasks.