  Cross-lingual alignment of word embeddings play an important role in
knowledge transfer across languages, for improving machine translation and
other multi-lingual applications. Current unsupervised approaches rely on
similarities in geometric structure of word embedding spaces across languages,
to learn structure-preserving linear transformations using adversarial networks
and refinement strategies. However, such techniques, in practice, tend to
suffer from instability and convergence issues, requiring tedious fine-tuning
for precise parameter setting. This paper proposes BioSpere, a novel framework
for unsupervised mapping of bi-lingual word embeddings onto a shared vector
space, by combining adversarial initialization and refinement procedure with
point set registration algorithm used in image processing. We show that our
framework alleviates the shortcomings of existing methodologies, and is
relatively invariant to variable adversarial learning performance, depicting
robustness in terms of parameter choices and training losses. Experimental
evaluation on parallel dictionary induction task demonstrates state-of-the-art
results for our framework on diverse language pairs.
