A Deep Neural Network Approach To Parallel Sentence Extraction
Table 1: Precision, recall and F1 scores at decision threshold value ρ maximizing the F1 score on our test set.
['[BOLD] Model', '[BOLD] Noise ratio = 0%  [BOLD] P (%)', '[BOLD] Noise ratio = 0%  [BOLD] R (%)', '[BOLD] Noise ratio = 0%  [BOLD] F1 [BOLD]  (%)', '[BOLD] Noise ratio = 0%  [ITALIC]  [BOLD] ρ', '[BOLD] Noise ratio = 90%  [BOLD] P (%)', '[BOLD] Noise ratio = 90%  [BOLD] R (%)', '[BOLD] Noise ratio = 90%  [BOLD] F1 [BOLD]  (%)', '[BOLD] Noise ratio = 90%  [ITALIC]  [BOLD] ρ']
In this section we measure the precision, recall and F1 scores to compare both methods. In order to compare the feasibility of our approach on different degrees of comparability of comparable corpora, we insert noisy non-parallel sentences into the test set by substituting a defined number of target sentences with external target sentences from the held-out sentence pairs of the newstest2012 corpus. For example, with a noise ratio of 60%, 600 out of the 1,000 sentence pairs are not parallel, such that only 0.04% of the sentence pairs in the Cartesian product are truly parallel. We see that we are able to consistently outperform by a significant margin the results obtained with the baseline model. Our approach has a F1 improvement over the baseline model of 9.61% and 19.61% on the test set with a noise ratio of 0% and 90%, respectively. The objective of parallel sentence extraction is to increase the size of existing parallel corpora and to broaden the covered domains in order to improve the generalization of machine translation systems. To justify the utility of our proposed approach, we extract parallel sentences from interlanguage linked English-French Wikipedia articles and evaluate their quality by measuring the BLEU scores on SMT systems. We use these values as a rough estimate to represent the degree of comparability present in Wikipedia article pairs.