A Deep Neural Network Approach To Parallel Sentence Extraction
Table 3: BLEU scores obtained on the newstest2013 test set. Sentences is the number of sentences used to train the SMT systems. The Europarl row is the baseline SMT system trained on 500k sentences pairs from the Europarl corpus.
['[BOLD] Training Data', '[BOLD] Model', '[BOLD] BLEU', '[BOLD] Sentences']
To train the phrase-based translation systems Koehn et al. we use the Moses toolkit. We train two additional SMT systems by augmenting the training set with the extracted sentence pairs from the BiRNN and baseline models. Each system uses newstest2012 as tuning set and is evaluated on the English-French translation quality on newstest2013. Since both extracted parallel corpus are not on the same order of magnitude, we sorted the sentence pairs by similarity score in descending order and used the top 500k sentence pairs to train new SMT systems with training sets of equal size. When using the full extracted parallel corpus, we see that our approach improves the BLEU score over the baseline SMT system trained solely on the 500k sentence pairs of the Europarl corpus by 4.7 and about 0.8 for the system trained with the extra sentence pairs extracted with the baseline model. When we only use the top 500k sentence pairs with higher similarity score, our approach is on par with the baseline system. Thus, it confirms the quality of the top 500k ranked sentence pairs, such that we could lower the decision threshold value to extract more high-quality parallel sentences. Given the out-of-domain nature of our Wikipedia articles with respect to our training set, those results are encouraging because they show that our approach should adapt well on comparable corpora with a lower degree of comparability.