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Identifying Misaligned Spans in Parallel Corpora Using Change Point Detection

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Book cover Advances in Artificial Intelligence (Canadian AI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11489))

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Abstract

Parallel corpora are the basic resource for many multilingual natural language processing models. Recent advances in, e.g. neural machine translation have shown that the quality of the alignment in the corpus has a crucial impact on the quality of the resulting model, renewing interest in filtering automatically aligned corpora to increase their quality. In this contribution, we investigate the use of a fast change point detection method to detect possibly problematic parts of a parallel corpus. We demonstrate its performance on German-English corpora of 11k and 31k sentences, achieve a boundary identification performance above 80% and improve the detection of genuine parallel sentences up to 88%. To our knowledge this is the first application of change point detection to the problem of error detection in noisy corpora.

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Notes

  1. 1.

    https://cran.r-project.org/web/packages/ocp/index.html.

  2. 2.

    http://www.statmt.org/wmt13/translation-task.html.

  3. 3.

    Note that the scope of this paper is not to develop a better sentence alignment score, but to identify document boundaries to improve corpus filtering.

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Authors and Affiliations

  1. Carleton University, Ottawa, ON, K1S 5B6, Canada

    Andrea Pagotto

  2. National Research Council Canada, Ottawa, ON, K1A 0R6, Canada

    Patrick Littell, Yunli Wang & Cyril Goutte

Authors
  1. Andrea Pagotto
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  2. Patrick Littell
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  3. Yunli Wang
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  4. Cyril Goutte
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Corresponding author

Correspondence to Cyril Goutte .

Editor information

Editors and Affiliations

  1. University of Quebec in Montreal, Montreal, QC, Canada

    Marie-Jean Meurs

  2. University of Toronto, Toronto, ON, Canada

    Frank Rudzicz

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Pagotto, A., Littell, P., Wang, Y., Goutte, C. (2019). Identifying Misaligned Spans in Parallel Corpora Using Change Point Detection. In: Meurs, MJ., Rudzicz, F. (eds) Advances in Artificial Intelligence. Canadian AI 2019. Lecture Notes in Computer Science(), vol 11489. Springer, Cham. https://doi.org/10.1007/978-3-030-18305-9_16

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  • DOI: https://doi.org/10.1007/978-3-030-18305-9_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-18304-2

  • Online ISBN: 978-3-030-18305-9

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