In Chapters 10 and 12, we focused on two common usages of recurrent neural networks and transformer networks: acceptors and transducers. In this chapter, we discuss a third architecture for both recurrent neural networks and transformer networks: encoder-decoder methods. We introduce three encoder-decoder architectures, which enable important NLP applications such as machine translation. In particular, we discuss the sequence-to-sequence method of Sutskever et al. (2014), which couples an encoder long short-term memory with a decoder long short-term memory. We follow this method with the approach of Bahdanau et al. (2015), which extends the previous decoder with an attention component, which produces a different encoding of the source text for each decoded word. Last, we introduce the complete encoder-decoder transformer network, which relies on three attention mechanisms: one within the encoder (which we discussed in Chapter 12), a similar one that operates over decoded words, and, importantly, an attention component that connects the input words with the decoded ones.