Seq2Seq Transformer

Seq2Seq Transformers are neural network architectures designed to translate input sequences into output sequences, addressing tasks ranging from machine translation and grammatical error correction to activity recognition and artistic analysis. Current research focuses on improving these models by incorporating additional information, such as temporal context, relational structures, and bidirectional processing, often within the framework of pre-trained Transformer models like T5 or BERT. These advancements lead to significant performance gains across diverse applications, highlighting the versatility and power of Seq2Seq Transformers for various sequence-to-sequence problems.