# Towards a Universal Semantic Dictionary
## 5 Experimental Results
### Experiments Using Three Languages  
Finally, a multilingual experiment was carried out where the system was trained on three languages--English, Italian, and Spanish--at the same time. During training, the system learns three different translation matrices, one for English-multilingual, one for Italian-multilingual, and one for Spanish-multilingual space mapping. For example, to learn the English-multilingual translation matrix, both the English-Italian and the English-Spanish dictionaries are used, according to Equation (3). Batches are homogeneous, but two following batches are always different in terms of the language origins of the contained data. That is, first an English-Italian batch is fed to the system, then an English-Spanish batch, after that an Italian-Spanish batch, and so on. First, bilingual models were trained to compare them later with the multilingual system. The results of the bilingual models are summarized in Table 12. The results are best on the Italian-Spanish task. Next, the system was trained  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Eng-Ita**} & \multicolumn{3}{c}{**Ita-Eng**} \\ \hline
**Precision** & **@1** & **@5** & **@10** & **@1** & **@5** & **@10** \\ \hline Train:PanLex-Testold & 0.1960 & 0.3087 & 0.3440 & 0.1838 & 0.3059 & 0.3443 \\ Train:PanLex-Testnew & 0.1812 & 0.2858 & 0.3196 & 0.1668 & 0.2835 & 0.3213 \\ Train:Dinu-Test:new & **0.2295** & **0.4171** & **0.4839** & 0.2227 & 0.3763 & 0.4199 \\ Train:Dinu+PanLex-Testnew & 0.2295 & 0.3712 & 0.4275 & **0.2498** & **0.4026** & **0.4495** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparing Dinu’s and PanLex data on the PanLex test set.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{3}{c}{**Eng-Ita**} & \multicolumn{3}{c}{**Ita-Eng**} \\ \hline
**Precision** & **@1** & **@5** & **@10** & **@1** & **@5** & **@10** \\ \hline Original & **0.3770** & **0.5647** & **0.6245** & 0.3103 & **0.5018** & 0.5474 \\ Cont. from 2000 & 0.3426 & 0.5256 & 0.5802 & **0.3229** & 0.4882 & **0.5535** \\ Cont. from 3000 & 0.3535 & 0.5416 & 0.5970 & 0.3229 & 0.4840 & 0.5465 \\ Cont. from 4000 & 0.3510 & 0.5273 & 0.5911 & 0.3118 & 0.4701 & 0.5243 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Continuing the baseline system with the PanLex data.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Eng-Ita**} & \multicolumn{3}{c}{**Ita-Eng**} \\ \hline
**Precision** & **@1** & **@5** & **@10** & **@1** & **@5** & **@10** \\ \hline Train:Dinu-Testold & 0.3770 & 0.5647 & 0.6245 & 0.3103 & 0.5018 & 0.5474 \\ Train:Dinu-Test:new & **0.3560** & **0.5407** & **0.5978** & **0.2917** & **0.4792** & **0.5215** \\ Train:PanLex-Testnew & 0.1360 & 0.2309 & 0.2594 & 0.1361 & 0.2556 & 0.2965 \\ Train:Dinu+PanLex-Test:new & 0.2930 & 0.4349 & 0.4861 & 0.2910 & 0.4556 & 0.5090 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Word reduction of the new test sets.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Eng-Ita**} & \multicolumn{3}{c}{**Ita-Eng**} \\ \hline
**Precision** & **@1** & **@5** & **@10** & **@1** & **@5** & **@10** \\ \hline Train:Dinu-Testold & 0.3770 & 0.5647 & 0.6245 & 0.3103 & 0.5018 & 0.5474 \\ Train:Dinu-Test:new & **0.3560** & **0.5407** & **0.5978** & **0.2917** & **0.4792** & **0.5215** \\ Train:PanLex-Testnew & 0.1360 & 0.2309 & 0.2594 & 0.1361 & 0.2556 & 0.2965 \\ Train:Dinu+PanLex-Test:new & 0.2930 & 0.4349 & 0.4861 & 0.2910 & 0.4556 & 0.5090 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparing Dinu’s and PanLex data on [PERSON]’s test set.  
using all three languages at the same time. During the training process, the model was evaluated on the bilingual test datasets, for which the results are shown in Table 13. The obtained results show that no advantage was achieved by extending the number of languages, since the multilingual model performs worse than any of the pairwise bilingual models.