  Parallel sentence extraction is a task addressing the data sparsity problem
found in multilingual natural language processing applications. We propose an
end-to-end deep neural network approach to detect translational equivalence
between sentences in two different languages. In contrast to previous
approaches, which typically rely on multiples models and various word alignment
features, by leveraging continuous vector representation of sentences we remove
the need of any domain specific feature engineering. Using a siamese
bidirectional recurrent neural networks, our results against a strong baseline
based on a state-of-the-art parallel sentence extraction system show a
significant improvement in both the quality of the extracted parallel sentences
and the translation performance of statistical machine translation systems. We
believe this study is the first one to investigate deep learning for the
parallel sentence extraction task.
