  Powerful sentence encoders trained for multiple languages are on the rise.
These systems are capable of embedding a wide range of linguistic properties
into vector representations. While explicit probing tasks can be used to verify
the presence of specific linguistic properties, it is unclear whether the
vector representations can be manipulated to indirectly steer such properties.
We investigate the use of a geometric mapping in embedding space to transform
linguistic properties, without any tuning of the pre-trained sentence encoder
or decoder. We validate our approach on three linguistic properties using a
pre-trained multilingual autoencoder and analyze the results in both
monolingual and cross-lingual settings.
