Learning Bilingual Word Embeddings Using Lexical Definitions
Table 2: Results of the word translation task.
['Language Metric', 'En-Fr  [ITALIC] P@1', 'En-Fr  [ITALIC] P@5', 'Fr-En  [ITALIC] P@1', 'Fr-En  [ITALIC] P@5', 'En-Es  [ITALIC] P@1', 'En-Es  [ITALIC] P@5', 'Es-En  [ITALIC] P@1', 'Es-En  [ITALIC] P@5']
Results. i) training with bilingual strong pairs only BilLex(PS), (ii) with directly induced pair added BilLex(PS+PD), and (iii) with all three types of word pairs BilLex(PS+PD+PI). BilLex(PS+PD+PI) thereof, offers consistently better performance in all settings, which implies that the induced word pairs are effective in improving the cross-lingual learning of lexical semantics. Among the baseline models, the unsupervised MUSE outperforms the other four supervised ones. We also discover that for the word translation task, the supervised models with coarse alignment such as BiCVM and BilBOWA do not perform as well as the models with word-level supervision, such as BiSkip and supervised MUSE. Our best BilLex outperforms unsupervised MUSE by 4.4∼5.7% of P@1 between En and Fr, and by 0.3∼1.8% between En and Es. The reason why the settings between En and Fr achieve better performance is that there are much fewer bilingual definitions between En and Es.