Comparative study of sequence-to-sequence models: From RNNs to transformers

In this comprehensive exploration of sequence-to-sequence models in Natural Language Processing (NLP), we have traced the trajectory of their evolution and contributions. Starting from foundational Recurrent Neural Networks (RNNs) to the revolutionary capabilities of Long Short-Term Memory (LSTM), In this comprehensive exploration of sequence-to-sequence models in Natural Language Processing (NLP), we have meticulously traced the trajectory of their evolution and impactful contributions. From the foundational Recurrent Neural Networks (RNNs) to the revolutionary capabilities of Long Short-Term Memory (LSTM), as well as the transformative innovations brought forth by Transformers and BERT, this review eloquently highlights the monumental advancements that have fundamentally reshaped our understanding and generation of language. The crux of our comparative analysis lies in its ability to spotlight the distinctive strengths and limitations inherent in each model. Through an intricate examination, we uncover their nuanced applications across a diverse spectrum of NLP tasks. Particularly noteworthy is the pivotal role played by Transformers and the transformative Bidirectional Encoder Representations from Transformers (BERT). The paper concludes with a summary and outlook of the entire paper.