The research described in this paper was primarily conducted in the project 'Linked Open Dictionaries' (LiODi, 2015-2020), funded by the German Ministry for Education and Research (BMBF) as an Independent Research Group on eHumanities. The conversion of FreeDict dictionaries into TIAD-TSV data that we used for estimating the prediction threshold was performed in the context of the Research and Innovation Action "Pret-a-LLOD. Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors" funded in the European Union's Horizon 2020 research and innovation programme under grant agreement No 825182.

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.