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README.md

A Set of Recommendations for Assessing Human–Machine Parity in Language Translation

This repository contains all experimental data described and analysed in:

@unpublished{laeubli2019parity,
  Author = {Läubli, Samuel and Casthilo, Sheila and Neubig, Graham and Sennrich, Rico and Shen, Qinlan and Toral, Antonio},
  Title  = {A Set of Recommendations for Assessing Human--Machine Parity in Language Translation},
  Year   = {2019},
  Note   = {Under review}}

Structure

Subdirectory Reference Main Finding
raters Section 3 Employing professional translators rather than crowd workers and researchers increases the rating gap between human and machine translation.
linguistic-context Section 4 Evaluating full documents rather than isolated sentences increases the rating gap between human and machine translation.
reference-translations/quality Section 5.1 Machine translation contains significantly more incorrect words, omissions, mistranslated names, and word order errors than human translation in Hassan et al.'s (2018) dataset.
reference-translations/directionality Section 5.2 Translated texts are simpler than original texts, and in turn easier to machine translate.

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experimental data for paper "A Set of Recommendations for Assessing Human–Machine Parity in Language Translation"

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