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Automatic Machine Translation for Arabic Tweets

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Intelligent Natural Language Processing: Trends and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 740))

Abstract

Twitter is a continuous and unlimited source of data in natural language, which is particularly unstructured, highly noisy and short, making it difficult to deal with traditional approaches to automatic Natural Language Processing (NLP). The current research focus on the implementation of a phrase-based statistical machine translation system for tweets, from a complex and a morphological rich language, Arabic, into English. The first challenge is prepossessing the highly noisy data collected from Twitter, for both the source and target languages. A special attention is given to the pre-processing of Arabic tweets. The second challenge is related to the lack of parallel corpora for Arabic-English tweets. Thus, an out-of-domain corpus was incorporated for training a translation model and an adaptation strategy of a bigger language model for English tweets was used in the training step. Our evaluations confirm that pre-processing tweets of the source and target languages improves the performance of the statistical machine translation system. In addition, using an in-domain data for the language model and the tuning set, showed a better performance of the statistical machine translation system from Arabic to English tweets. An improvement of 4 pt. BLEU was realized.

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Notes

  1. 1.

    https://about.twitter.com/fr/company.

  2. 2.

    https://support.twitter.com/articles/20172133.

  3. 3.

    http://www.isi.edu/licensed-sw/pharaoh/.

  4. 4.

    http://www.statmt.org/moses.

  5. 5.

    https://dev.twitter.com/streaming/public.

  6. 6.

    http://twitter4j.org/en/index.html.

  7. 7.

    The dictionary contains 44983 pairs of words, available on-line at: http://noisy-text.github.io/2015/norm-shared-task.html.

  8. 8.

    The UN parallel corpus is available at: http://www.un.org/en/documents/ods/.

  9. 9.

    http://www.statmt.org/moses/?n=FactoredTraining.PrepareTraining.

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Authors and Affiliations

  1. Université du Québec À Montréal, Montreal, Canada

    Fatma Mallek, Ngoc Tan Le & Fatiha Sadat

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  1. Fatma Mallek
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  2. Ngoc Tan Le
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  3. Fatiha Sadat
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Correspondence to Fatma Mallek .

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Editors and Affiliations

  1. The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

  2. Faculty of Computers and Information Technology, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  3. Faculty of Computers and Information, Ain Shams University, Cairo, Egypt

    Fahmy Tolba

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Mallek, F., Tan Le, N., Sadat, F. (2018). Automatic Machine Translation for Arabic Tweets. In: Shaalan, K., Hassanien, A., Tolba, F. (eds) Intelligent Natural Language Processing: Trends and Applications. Studies in Computational Intelligence, vol 740. Springer, Cham. https://doi.org/10.1007/978-3-319-67056-0_6

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