Effective Parallel Corpus Mining using Bilingual Sentence Embeddings
Table 3: Precision at N (P@N) of target sentence retrieval on the UN corpus. Models attempt to select the true translation target for a source sentence from the entire corpus (11.3 million aligned sentence pairs.)
['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']
Once again we first use P@N as the evaluation metric for target retrieval, for N=1, 3, 10. We evaluate the two random sampling models and a hard negative model with 20 hard negatives for each example. Adding hard negatives boosts the performance on all metrics, improving the P@1 metric more than 10% absolute in both en-fr and en-es – 48.9% for en-fr and 54.9% for en-es.