Paraphrase Detection on Noisy Subtitles in Six Languages

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

We perform automatic paraphrase detection on subtitle data from the Opusparcus corpus comprising six European languages: German, English, Finnish, French, Russian, and Swedish. We train two types of supervised sentence embedding models: a word-averaging (WA) model and a gated recurrent averaging network (GRAN) model. We find out that GRAN outperforms WA and is more robust to noisy training data. Better results are obtained with more and noisier data than less and cleaner data. Additionally, we experiment on other datasets, without reaching the same level of performance, because of domain mismatch between training and test data.
Original languageEnglish
Title of host publicationProceedings of the 2018 EMNLP Workshop W-NUT : The 4th Workshop on Noisy User-generated Text
EditorsWei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
Number of pages10
Place of PublicationStroudsburg
PublisherAssociation for Computational Linguistics
Publication date1 Nov 2018
Pages64-73
ISBN (Electronic)978-1-948087-79-7
Publication statusPublished - 1 Nov 2018
MoE publication typeA4 Article in conference proceedings
EventWorkshop on Noisy User-generated Text - Brussels, Belgium
Duration: 1 Nov 20181 Nov 2018
Conference number: 4

Fields of Science

  • 113 Computer and information sciences
  • 6121 Languages

Cite this

Sjöblom, E. I., Creutz, M. J. P., & Aulamo, M. J. (2018). Paraphrase Detection on Noisy Subtitles in Six Languages. In W. Xu, A. Ritter, T. Baldwin, & A. Rahimi (Eds.), Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text (pp. 64-73). Stroudsburg: Association for Computational Linguistics.
Sjöblom, Eetu Ilari ; Creutz, Mathias Johan Philip ; Aulamo, Mikko Juhani. / Paraphrase Detection on Noisy Subtitles in Six Languages. Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text. editor / Wei Xu ; Alan Ritter ; Tim Baldwin ; Afshin Rahimi. Stroudsburg : Association for Computational Linguistics, 2018. pp. 64-73
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title = "Paraphrase Detection on Noisy Subtitles in Six Languages",
abstract = "We perform automatic paraphrase detection on subtitle data from the Opusparcus corpus comprising six European languages: German, English, Finnish, French, Russian, and Swedish. We train two types of supervised sentence embedding models: a word-averaging (WA) model and a gated recurrent averaging network (GRAN) model. We find out that GRAN outperforms WA and is more robust to noisy training data. Better results are obtained with more and noisier data than less and cleaner data. Additionally, we experiment on other datasets, without reaching the same level of performance, because of domain mismatch between training and test data.",
keywords = "113 Computer and information sciences, 6121 Languages",
author = "Sj{\"o}blom, {Eetu Ilari} and Creutz, {Mathias Johan Philip} and Aulamo, {Mikko Juhani}",
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Sjöblom, EI, Creutz, MJP & Aulamo, MJ 2018, Paraphrase Detection on Noisy Subtitles in Six Languages. in W Xu, A Ritter, T Baldwin & A Rahimi (eds), Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text. Association for Computational Linguistics, Stroudsburg, pp. 64-73, Workshop on Noisy User-generated Text, Brussels, Belgium, 01/11/2018.

Paraphrase Detection on Noisy Subtitles in Six Languages. / Sjöblom, Eetu Ilari; Creutz, Mathias Johan Philip; Aulamo, Mikko Juhani.

Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text. ed. / Wei Xu; Alan Ritter; Tim Baldwin; Afshin Rahimi. Stroudsburg : Association for Computational Linguistics, 2018. p. 64-73.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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T1 - Paraphrase Detection on Noisy Subtitles in Six Languages

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Y1 - 2018/11/1

N2 - We perform automatic paraphrase detection on subtitle data from the Opusparcus corpus comprising six European languages: German, English, Finnish, French, Russian, and Swedish. We train two types of supervised sentence embedding models: a word-averaging (WA) model and a gated recurrent averaging network (GRAN) model. We find out that GRAN outperforms WA and is more robust to noisy training data. Better results are obtained with more and noisier data than less and cleaner data. Additionally, we experiment on other datasets, without reaching the same level of performance, because of domain mismatch between training and test data.

AB - We perform automatic paraphrase detection on subtitle data from the Opusparcus corpus comprising six European languages: German, English, Finnish, French, Russian, and Swedish. We train two types of supervised sentence embedding models: a word-averaging (WA) model and a gated recurrent averaging network (GRAN) model. We find out that GRAN outperforms WA and is more robust to noisy training data. Better results are obtained with more and noisier data than less and cleaner data. Additionally, we experiment on other datasets, without reaching the same level of performance, because of domain mismatch between training and test data.

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Sjöblom EI, Creutz MJP, Aulamo MJ. Paraphrase Detection on Noisy Subtitles in Six Languages. In Xu W, Ritter A, Baldwin T, Rahimi A, editors, Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text. Stroudsburg: Association for Computational Linguistics. 2018. p. 64-73