Sequence to Sequence Model

Sequence-to-sequence (Seq2Seq) models are neural networks designed to map input sequences to output sequences, primarily used for tasks like machine translation and speech recognition. Current research focuses on improving model efficiency and generalization capabilities, exploring architectures like Transformers and LSTMs, and addressing challenges such as handling long sequences and achieving compositional generalization. These advancements have significant implications across diverse fields, enabling improved performance in natural language processing, speech processing, and anomaly detection, among others.