Summary

In this chapter, we covered how to build sequence-to-sequence models from scratch. We learned how to code up our encoder and decoder components individually and how to integrate them into a single model that is able to translate sentences from one language into another.

Although our sequence-to-sequence model, which consists of an encoder and a decoder, is useful for sequence translation, it is no longer state-of-the-art. In the last few years, combining sequence-to-sequence models with attention models has been done to achieve state-of-the-art performance.

In the next chapter, we will discuss how attention networks can be used in the context of sequence-to-sequence learning and show how we can use both techniques to build a chat bot.