  Introducing factors, that is to say, word features such as linguistic
information referring to the source tokens, is known to improve the results of
neural machine translation systems in certain settings, typically in recurrent
architectures. This study proposes enhancing the current state-of-the-art
neural machine translation architecture, the Transformer, so that it allows to
introduce external knowledge. In particular, our proposed modification, the
Factored Transformer, uses linguistic factors that insert additional knowledge
into the machine translation system. Apart from using different kinds of
features, we study the effect of different architectural configurations.
Specifically, we analyze the performance of combining words and features at the
embedding level or at the encoder level, and we experiment with two different
combination strategies. With the best-found configuration, we show improvements
of 0.8 BLEU over the baseline Transformer in the IWSLT German-to-English task.
Moreover, we experiment with the more challenging FLoRes English-to-Nepali
benchmark, which includes both extremely low-resourced and very distant
languages, and obtain an improvement of 1.2 BLEU.
