Translation services are generally trained on parallel text, where we believe a given sentence is the translation of another sentence. You don't specify which

Translation services are generally trained on parallel text, where we believe a given sentence is the translation of another sentence. You don't specify which word is the translation of which word. That would take a lot of human effort, and wouldn't even have a correct answer in some cases. You just throw a lot of parallel sentences at a machine learning algorithm and let it infer how to translate the words, somewhere inside an inscrutable model. My guess, then, is that "Daesh" is a relatively new word that is not represented very well in their parallel corpus of Arabic text. Suppose it has just one sentence of parallel text that mentions Daesh, and that sentence also mentions Saudi Arabia, but the words appear in a different order in English and Arabic. It doesn't have enough evidence to figure out that the word order is different, and it ends up aligning Arabic "Daesh" with English "Saudi Arabia". Now, Google Translate at least has a fallback on a dictionary, and you can see this when you enter a single word. But maybe Bing's dictionary isn't recent enough to include "Daesh". (Aside: NLP programmers, from beginners to experts, don't make enough of a habit of using recent corpora and keeping them updated. The Brown corpus is 55 years old and is frequently used in tutorials to make models of English. The Penn Treebank is 24 years old. Pierre Vinken, the subject of the first sentence in the Penn Treebank, is dead.)