Lower absolute scores for Italian and German compared to the ones reported for English are due to multiple factors, as discussed previously by : 1) the AR model uses less linguistic constraints for DE and IT; 2) distributional word vectors are induced from smaller corpora; 3) linguistic phenomena (e.g., cases and compounding in DE) contribute to data sparsity and also make the DST task more challenging.
it is important to stress the consistent gains over the vector space specialised by the state-of-the-art ATTRACT-REPEL model across all three test languages.