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        <title>Deep Neural Networks at the Service of Multilingual Parallel Sentence Extraction</title>
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        <namePart type="given">Ahmad</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Bender</namePart>
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            <namePart type="given">Leon</namePart>
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            <namePart type="given">Pierre</namePart>
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    <abstract>Wikipedia provides an invaluable source of parallel multilingual data, which are in high demand for various sorts of linguistic inquiry, including both theoretical and practical studies. We introduce a novel end-to-end neural model for large-scale parallel data harvesting from Wikipedia. Our model is language-independent, robust, and highly scalable. We use our system for collecting parallel German-English, French-English and Persian-English sentences. Human evaluations at the end show the strong performance of this model in collecting high-quality parallel data. We also propose a statistical framework which extends the results of our human evaluation to other language pairs. Our model also obtained a state-of-the-art result on the German-English dataset of BUCC 2017 shared task on parallel sentence extraction from comparable corpora.</abstract>
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%0 Conference Proceedings
%T Deep Neural Networks at the Service of Multilingual Parallel Sentence Extraction
%A Aghaebrahimian, Ahmad
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F aghaebrahimian-2018-deep
%X Wikipedia provides an invaluable source of parallel multilingual data, which are in high demand for various sorts of linguistic inquiry, including both theoretical and practical studies. We introduce a novel end-to-end neural model for large-scale parallel data harvesting from Wikipedia. Our model is language-independent, robust, and highly scalable. We use our system for collecting parallel German-English, French-English and Persian-English sentences. Human evaluations at the end show the strong performance of this model in collecting high-quality parallel data. We also propose a statistical framework which extends the results of our human evaluation to other language pairs. Our model also obtained a state-of-the-art result on the German-English dataset of BUCC 2017 shared task on parallel sentence extraction from comparable corpora.
%U https://aclanthology.org/C18-1116/
%P 1372-1383

Markdown (Informal)

[Deep Neural Networks at the Service of Multilingual Parallel Sentence Extraction](https://aclanthology.org/C18-1116/) (Aghaebrahimian, COLING 2018)

• Deep Neural Networks at the Service of Multilingual Parallel Sentence Extraction (Aghaebrahimian, COLING 2018)

ACL

• Ahmad Aghaebrahimian. 2018. Deep Neural Networks at the Service of Multilingual Parallel Sentence Extraction. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1372–1383, Santa Fe, New Mexico, USA. Association for Computational Linguistics.