Extracting Parallel Sentences with Bidirectional Recurrent Neural Networks to Improve Machine Translation
Table 1: Precision (P), recall (R) and F1 scores where the decision threshold ρ maximizes the area under the precision-recall curve of the test sets with noise ratios of 0%, 50% and 90%.
['[BOLD] Noise', '[BOLD] Model', '[BOLD] newstest2012  [BOLD] P (%)', '[BOLD] newstest2012  [BOLD] R (%)', '[BOLD] newstest2012  [BOLD] F1 (%)', '[BOLD] newstest2012  [ITALIC]  [BOLD] ρ', '[BOLD] Europarl  [BOLD] P (%)', '[BOLD] Europarl  [BOLD] R (%)', '[BOLD] Europarl  [BOLD] F1 (%)', '[BOLD] Europarl  [ITALIC]  [BOLD] ρ']
4.1.6 Evaluation on noisy parallel corpora We see that BiRNN is able to constantly outperform the results obtained with the baseline by a significant margin. By using newstest2012 as out-of-domain test sets, our approach gets an absolute improvement in the F1 score of 10.99%, 13.03% and 23.53%. These curves illustrate the consistency of our approach, whereas the performance of the baseline is greatly impacted when the number of non-parallel sentences in the test set is high.