A sequence to sequence (or seq2seq) model is neural architecture used for translation (and other tasks) which consists of an encoder and a decoder.

The encoder/decoder architecture has obvious promise for machine translation, and has been successfully applied this way. Encoding an input to a small number of hidden nodes which can effectively be decoded to a matching string requires machine learning to learn an efficient representation of the essence of the strings.

In addition to translation, seq2seq models have been used in a number of other NLP tasks such as summarization and image captioning.

Related Links

• tf-seq2seq

• Describing Multimedia Content using Attention-based Encoder--Decoder Networks

• Show and Tell: A Neural Image Caption Generator

• Attend to You: Personalized Image Captioning with Context Sequence Memory Networks