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Machine Translation

, Volume 32, Issue 3, pp 195–215 | Cite as

Quantitative fine-grained human evaluation of machine translation systems: a case study on English to Croatian

  • Filip KlubičkaEmail author
  • Antonio Toral
  • Víctor M. Sánchez-Cartagena
Article

Abstract

This paper presents a quantitative fine-grained manual evaluation approach to comparing the performance of different machine translation (MT) systems. We build upon the well-established multidimensional quality metrics (MQM) error taxonomy and implement a novel method that assesses whether the differences in performance for MQM error types between different MT systems are statistically significant. We conduct a case study for English-to-Croatian, a language direction that involves translating into a morphologically rich language, for which we compare three MT systems belonging to different paradigms: pure phrase-based, factored phrase-based and neural. First, we design an MQM-compliant error taxonomy tailored to the relevant linguistic phenomena of Slavic languages, which made the annotation process feasible and accurate. Errors in MT outputs were then annotated by two annotators following this taxonomy. Subsequently, we carried out a statistical analysis which showed that the best-performing system (neural) reduces the errors produced by the worst system (pure phrase-based) by more than half (54%). Moreover, we conducted an additional analysis of agreement errors in which we distinguished between short (phrase-level) and long distance (sentence-level) errors. We discovered that phrase-based MT approaches are of limited use for long distance agreement phenomena, for which neural MT was found to be especially effective.

Keywords

Neural machine translation Statistical machine translation Phrase-based machine translation Factored models Human evaluation Error annotation Multidimensional quality metrics (MQM) 

Notes

Acknowledgements

We would like to extend our thanks to Maja Popović, who provided invaluable advice, and Denis Kranjčić, who performed the annotation together with Filip Klubička, first author of the paper. This research was partly funded by the ADAPT Centre, which is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund. This research has also received funding from the European Union Seventh Framework Programme FP7/2007-2013 under Grant agreement PIAP-GA-2012-324414 (Abu-MaTran) and the Swiss National Science Foundation Grant 74Z0_160501 (ReLDI).

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© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Dublin Institute of TechnologyDublinIreland
  2. 2.University of GroningenGroningenThe Netherlands
  3. 3.Prompsit Language EngineeringAlacantSpain

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