Multi-Agent Cross-Translated Diversification for Unsupervised Machine Translation
Table 4: Reconstruction BLEU scores of MAD and MACD in different languages for the WMT unsupervised tasks. Lower BLEU means more diverse.
['[BOLD] Method', '[BOLD] En-Fr-En', '[BOLD] Fr-En-Fr', '[BOLD] En-De-En', '[BOLD] De-En-De', '[BOLD] En-Ro-En', '[BOLD] Ro-En-Ro']
Having argued that cross-translation creates extra information for the supervised MT model to leverage on, we hypothesize that such extra information can be measurable by the diversity of the generated data. To measure this, we compute the reconstruction BLEU and compare the scores for MAD and MACD (both 2nd level, n=2) in the WMT En-Fr, En-De and En-Ro tasks. The scores are obtained by using the first agent to translate monolingual data in language s to t and then the second agent to translate those translations back to language s. After that, a BLEU score is measured by comparing the reconstructed text with the original text. In MAD, the first and second agents are identical, while they are distinct for MACD.