The sequence to sequence (Seq2Seq) paper by Ilya Sutskever, Oriol Vinyals, and Quoc V. Le set a new standard for sequence to sequence translation by introducing a novel approach that successfully maps a sentence to a fixed-length vector representation and back to a sentence. This groundbreaking work utilized a pair of Long Short-Term Memory (LSTM) networks, where one LSTM encodes the input sequence into a vector, and another decodes this vector to generate the output sequence. This model was particularly innovative because it enabled the handling of variable-length input and output sequences, which was a significant limitation in previous models.

The Seq2Seq model was one of the first to demonstrate that deep learning techniques could effectively be applied to complex sequence-based tasks like language translation. It showcased the potential of deep neural networks to understand and generate human language by learning from large amounts of data. The success of this model led to substantial improvements in machine translation quality, influencing the development of more advanced architectures like the Transformer model and further advancing the field of natural language processing (NLP).

The paper's influence extended beyond translation, impacting various other sequence-based tasks such as text summarization, speech recognition, and even bioinformatics, where mapping sequences of biological data to meaningful patterns is crucial.