Sequence-to-sequence (seq2seq) models in NLP are used to convert sequences of Type A to sequences of Type B. For example, translation of English sentences to German sentences is a sequence-to-sequence task.
Recurrent Neural Network (RNN) based sequence-to-sequence models have garnered a lot of traction ever since they were introduced in 2014. Most of the data in the current world are in the form of sequences – it can be a number sequence, text sequence, a video frame sequence or an audio sequence.
The performance of these seq2seq models was further enhanced with the addition of the Attention Mechanism in 2015. How quickly advancements in NLP have been happening in the last 5 years – incredible!
These sequence-to-sequence models are pretty versatile and they are used in a variety of NLP tasks, such as:
- Machine Translation
- Text Summarization
- Speech Recognition
- Question-Answering System, and so on