  Bilingual Lexical Induction (BLI) is a core challenge in NLP, it relies on
the relative isomorphism of individual embedding spaces. Existing attempts
aimed at controlling the relative isomorphism of different embedding spaces
fail to incorporate the impact of semantically related words in the model
training objective. To address this, we propose GARI that combines the
distributional training objectives with multiple isomorphism losses guided by
the graph attention network. GARI considers the impact of semantical variations
of words in order to define the relative isomorphism of the embedding spaces.
Experimental evaluation using the Arabic language data set shows that GARI
outperforms the existing research by improving the average P@1 by a relative
score of up to 40.95% and 76.80% for in-domain and domain mismatch settings
respectively. We release the codes for GARI at
https://github.com/asif6827/GARI.
