Well.... It is one of the oldest ideas in machine translation. In the late 1950s, Richens proposed an MT system that used an intermediate representation called

Well.... It is one of the oldest ideas in machine translation. In the late 1950s, Richens proposed an MT system that used an intermediate representation called an "interlingua." For each language, the system has two components, one that maps a natural language onto the interlingua, and a second that converts the interlingua back into a natural language. Use different source and destination pairs and--bam! you've got a multi-way translator. However, this only works if a) the interlingua is rich enough to capture the semantics of the to-be-translated text and b) the conversions between natural languages and interlingua also preserves those semantics. In practice, neither has worked pretty well. In the 1960s, the interlingua was typically hand-crafted and rules were either hand written, or later, induced, to convert natural languages to/from it. Since people have been working on this for 60 years, you can probably image how well that works[]. The clever bit here is that you don't really need to do that. If you have enough data, the LSTMs can learn it on their own (see Figure 2, for example, where semantically-related sentences from multiple languages end up in the same neighborhoods). [] Actually, somewhat better than you'd think. It certainly wouldn't have done Pushkin any justice, but it was surprisingly decent on weather reports (etc).