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Parallel sentence extraction is a task addressing the data sparsity problemfound in multilingual natural language processing applications. We propose anend-to-end deep neural network approach to detect translational equivalencebetween sentences in two different languages. In contrast to previousapproaches, which typically rely on multiples models and various word alignmentfeatures, by leveraging continuous vector representation of sentences we removethe need of any domain specific feature engineering. Using a siamesebidirectional recurrent neural networks, our results against a strong baselinebased on a state-of-the-art parallel sentence extraction system show asignificant improvement in both the quality of the extracted parallel sentencesand the translation performance of statistical machine translation systems. Webelieve this study is the first one to investigate deep learning for theparallel sentence extraction task.

A Deep Neural Network Approach To Parallel Sentence Extraction