Using targeted paraphrasing and monolingual crowdsourcing to improve translation

  • Authors:

  • Philip Resnik;Olivia Buzek;Yakov Kronrod;Chang Hu;Alexander J. Quinn;Benjamin B. Bederson

  • Affiliations:

  • University of Maryland, College Park, MD;University of Maryland, College Park, MD;University of Maryland, College Park, MD;University of Maryland, College Park, MD;University of Maryland, College Park, MD;University of Maryland, College Park, MD

  • Venue:
  • ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
  • Year:
  • 2013

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Abstract

Targeted paraphrasing is a new approach to the problem of obtaining cost-effective, reasonable quality translation, which makes use of simple and inexpensive human computations by monolingual speakers in combination with machine translation. The key insight behind the process is that it is possible to spot likely translation errors with only monolingual knowledge of the target language, and it is possible to generate alternative ways to say the same thing (i.e., paraphrases) with only monolingual knowledge of the source language. Formal evaluation demonstrates that this approach can yield substantial improvements in translation quality, and the idea has been integrated into a broader framework for monolingual collaborative translation that produces fully accurate, fully fluent translations for a majority of sentences in a real-world translation task, with no involvement of human bilingual speakers.