  Resources for the non-English languages are scarce and this paper addresses
this problem in the context of machine translation, by automatically extracting
parallel sentence pairs from the multilingual articles available on the
Internet. In this paper, we have used an end-to-end Siamese bidirectional
recurrent neural network to generate parallel sentences from comparable
multilingual articles in Wikipedia. Subsequently, we have showed that using the
harvested dataset improved BLEU scores on both NMT and phrase-based SMT systems
for the low-resource language pairs: English--Hindi and English--Tamil, when
compared to training exclusively on the limited bilingual corpora collected for
these language pairs.
