Effective Parallel Corpus Mining using Bilingual Sentence Embeddings
Table 2: Precision at N (P@N) results on the evaluation set for models built using the random negatives and (M) hard negatives. Models attempt to select the true translation target for a source sentence against 10M randomly selected targets.
['Negative Selection Approach', 'en-fr P@1', 'en-fr P@3', 'en-fr P@10', 'en-es P@1', 'en-es P@3', 'en-es P@10']
The model with random negatives provides a strong baseline for finding the right translation target, with a P@1 metric of 70.49% for en-fr and 67.81% for en-es. The augmented random negative model performs better than the base random negative model for en-es. However, the hard negative models outperform the random negative models across all metrics. Even with only 5 hard negatives, the P@1 metrics improved by 8% for en-fr and 3% for en-es. The addition of more hard negatives, however, does not always further improve performance.