Understanding the translation model

Machine translation is often done using so-called statistical machine translation, based on statistical models. This approach works very well, but a key issue is that, for every pair of languages, we need to rebuild the architecture. Thankfully, in 2014, Cho et al. (https://arxiv.org/pdf/1406.1078.pdf) came out with a paper that aims to solve this, and other problems, using the increasingly popular recurrent neural networks. The model is called sequence-to-sequence, and has the ability to be trained on any pair of languages by just providing the right amount of data. In addition, its power lies in its ability to match sequences of different lengths, such as in machine translation, where a sentence in English may have a different size when compared to a sentence in Spanish. Let's examine how these tasks...