  In this paper, we use the framework of neural machine translation to learn
joint sentence representations across six very different languages. Our aim is
that a representation which is independent of the language, is likely to
capture the underlying semantics. We define a new cross-lingual similarity
measure, compare up to 1.4M sentence representations and study the
characteristics of close sentences. We provide experimental evidence that
sentences that are close in embedding space are indeed semantically highly
related, but often have quite different structure and syntax. These relations
also hold when comparing sentences in different languages.
