ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs
Table 1: Results of sentence embedding mapping experiment in terms of precision@1 and precision@5. Our proposed ABSent only utilizes 20% of parallel training data and the equal size of unparalleled data for training, while all baselines take 100% training data with parallel labels. The best-performing method is highlighted in bold.
['Datasets Corpus', '[BOLD] Tatoeba deu→eng', '[BOLD] Tatoeba deu→eng', '[BOLD] Tatoeba eng→deu', '[BOLD] Tatoeba eng→deu', '[BOLD] Tatoeba spa→eng', '[BOLD] Tatoeba spa→eng', '[BOLD] Tatoeba eng→spa', '[BOLD] Tatoeba eng→spa', '[BOLD] Europarl deu→eng', '[BOLD] Europarl deu→eng', '[BOLD] Europarl eng→deu', '[BOLD] Europarl eng→deu', '[BOLD] Europarl spa→eng', '[BOLD] Europarl spa→eng', '[BOLD] Europarl eng→spa', '[BOLD] Europarl eng→spa']
Recall that we only use 20% of parallel sentences (true pairs) to train our model while all the other baselines utilize 100% of parallel sentence pairs for the training. We observe that our ABSent approach still significantly outperforms other baselines by a large margin. Take the deu→eng data from Tatoeba as an example. Our method achieves a precision@1 of 46.2% and a precision@5 of 65.5%, which are 18.6 and 22.9 absolute percentage points higher than the respective results of the best baseline. Similar trends can be observed for other datasets. Note that the results for different languages are not fully comparable due to different sizes of training data.