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DeepSubQE: Quality estimation for subtitle translations

Prabhakar Gupta,A. Nelakanti

Published 2020 in arXiv.org

ABSTRACT

Quality estimation (QE) for tasks involving language data is hard owing to numerous aspects of natural language like variations in paraphrasing, style, grammar, etc. There can be multiple answers with varying levels of acceptability depending on the application at hand. In this work, we look at estimating quality of translations for video subtitles. We show how existing QE methods are inadequate and propose our method DeepSubQE as a system to estimate quality of translation given subtitles data for a pair of languages. We rely on various data augmentation strategies for automated labelling and synthesis for training. We create a hybrid network which learns semantic and syntactic features of bilingual data and compare it with only-LSTM and only-CNN networks. Our proposed network outperforms them by significant margin.

PUBLICATION RECORD

  • Publication year

    2020

  • Venue

    arXiv.org

  • Publication date

    2020-04-22

  • Fields of study

    Mathematics, Linguistics, Computer Science

  • Identifiers
    arXiv 2004.13828
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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CONCEPTS

  • No concepts are published for this paper.

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