ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs
Table 5: Results of multilingual sentence embedding mapping experiment in terms of accuracy %) and precision@5 (%). The best-performing method is highlighted in bold. (Baseline A: Mikolov2013ExploitingSA Mikolov2013ExploitingSA, Baseline B: dinu2014improving dinu2014improving, Baseline C: Smith2017OfflineBW Smith2017OfflineBW, Baseline D: lample2018word lample2018word, Baseline E: Schwenk2017LearningJM Schwenk2017LearningJM)
['Datasets Corpus', '[BOLD] Tatoeba deu→spa', '[BOLD] Tatoeba deu→spa', '[BOLD] Tatoeba spa→deu', '[BOLD] Tatoeba spa→deu', '[BOLD] Europarl deu→spa', '[BOLD] Europarl deu→spa', '[BOLD] Europarl spa→deu', '[BOLD] Europarl spa→deu']
Though seq-to-seq models can learn full-fledged neural translation models, they do not fare particularly well in resource-constrained scenarios with limited training data. Particularly, even with a parallel sentence pair percentage of just 20%, our model outperforms many baselines that utilize the total amount of training data. Moreover, retrieval accuracies between two source languages German and Spanish obtained by our model are very competitive with baselines receiving supervision for that language pair. Note that in our method, we do not provide any direct pairwise mapping. This proves the effectiveness of our zero-shot Multilingual mapping.