• Languages have divergences, both structural and lexical, that make translation difficult. • The linguistic field of typology investigates some of these differences; languages can be classified by their position along typological dimensions like whether verbs precede their objects. • Encoder-decoder networks (either for RNNs or transformers) are composed of an encoder network that takes an input sequence and creates a contextualized representation of it, the context. This context representation is then passed to a decoder which generates a task-specific output sequence. • The attention mechanism in RNNs, and cross-attention in transformers, allows the decoder to view information from all the hidden states of the encoder. • For the decoder, choosing the single most probable token to generate at each step is called greedy decoding. • In beam search, instead of choosing the best token to generate at each timestep, we keep k possible tokens at each step. This fixed-size memory footprint k is called the beam width. • Machine translation models are trained on a parallel corpus, sometimes called a bitext, a text that appears in two (or more) languages. • Backtranslation is a way of making use of monolingual corpora in the target language by running a pilot MT engine backwards to create synthetic bitexts. • MT is evaluated by measuring a translation's adequacy (how well it captures the meaning of the source sentence) and fluency (how fluent or natural it is in the target language). Human evaluation is the gold standard, but automatic evaluation metrics like chrF, which measure character n-gram overlap with human translations, or more recent metrics based on embedding similarity, are also commonly used.