Morpho-syntactic information for automatic error analysis of statistical machine translation output

  • Authors:
  • Maja Popović;Hermann Ney;Adrià de Gispert;José B. Mariño;Deepa Gupta;Marcello Federico;Patrik Lambert;Rafael Banchs

  • Affiliations:
  • RWTH Aachen University, Aachen, Germany;RWTH Aachen University, Aachen, Germany;Universitat Politècnica de Catalunya (UPC), Barcelona, Spain;Universitat Politècnica de Catalunya (UPC), Barcelona, Spain;Centro per la Ricerca Scientifica e Tecnologica, Trento, Italy;Centro per la Ricerca Scientifica e Tecnologica, Trento, Italy;Universitat Politècnica de Catalunya (UPC), Barcelona, Spain;Universitat Politècnica de Catalunya (UPC), Barcelona, Spain

  • Venue:
  • StatMT '06 Proceedings of the Workshop on Statistical Machine Translation
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

Evaluation of machine translation output is an important but difficult task. Over the last years, a variety of automatic evaluation measures have been studied, some of them like Word Error Rate (WER), Position Independent Word Error Rate (PER) and BLEU and NIST scores have become widely used tools for comparing different systems as well as for evaluating improvements within one system. However, these measures do not give any details about the nature of translation errors. Therefore some analysis of the generated output is needed in order to identify the main problems and to focus the research efforts. On the other hand, human evaluation is a time consuming and expensive task. In this paper, we investigate methods for using of morpho-syntactic information for automatic evaluation: standard error measures WER and PER are calculated on distinct word classes and forms in order to get a better idea about the nature of translation errors and possibilities for improvements.