This paper describes our contribution to the Shared Task on
Translation Inference across Dictionaries (TIAD-2019). In our approach,
we construct a multi-lingual word embedding space by projecting new
languages in the feature space of a language for which a pretrained embedding model exists. We use the similarity of the word embeddings to
predict candidate translations. Even if our projection methodology is
rather simplistic, our system outperforms the other participating systems with respect to the F1 measure for the language pairs which we
predicted
