A linguistically motivated taxonomy for Machine Translation error analysis

Ângela Costa, Wang Ling, Tiago Luís, Rui Correia, Luisa Coheur

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Abstract

A detailed error analysis is a fundamental step in every natural lan- guage processing task, as to be able to diagnosis what went wrong will provide cues to decide which are the research directions to be followed. In this paper we focus on error analysis in Machine Translation. We deeply extend previous error taxonomies so that translation errors associated with Romance languages speci- ficities can be accommodated. Also, based on the proposed taxonomy, we carry out an extensive analysis of the errors generated by four di↵erent systems: two mainstream online translation systems Google Translate (Statistical) and Systran (Hybrid Machine Translation) and two in-house Machine Translation systems, in three scenarios representing di↵erent challenges in the translation from English to European Portuguese. Additionally, we comment on how distinct error types di↵erently impact translation quality.
Original languageEnglish
Pages (from-to)127-161
Number of pages34
JournalMachine Translation
Volume29
Issue number2
DOIs
Publication statusPublished - 2015

Keywords

  • Machine Translation
  • Error Taxonomy
  • Error Analysis
  • Romance Languages

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