The use of automation in the rendition of certain articles of the Saudi Commercial Law into English: a post-editing-based comparison of five machine translation systems.

Efforts to automate translation were made in the 1950s and 1960s, albeit with limited resources compared to current advanced standards. Machine translation is categorised under computational linguistics that examines employing computer software in the rendition of text from one language into another. The present paper seeks to compare five different machine translation systems for the sake of assessing the quality of their outputs in rendering certain articles of the Saudi Commercial Law into English through post-editing based on Human Translation Edit Rate. Each machine translation output is assessed against the same post-edited version, and the closest output to the post-edited version with regard to the use of the same lexicon and word order will achieve the lowest score. The lower the score of the machine translation output is, the higher quality it has. The paper then analyses the results of the Human Translation Edit Rate metric evaluation to ascertain as to whether or not high-quality machine translation outputs always produce acceptable Arabic-English legal translation. The present paper argues that the use of Human Translation Edit Rate metric is a useful tool for the sake of undertaking post-editing procedures as it is a combination of both human evaluation as well as automatic evaluation. It is also advantageous as it takes account of both the use of lexicon and word order. However, such metric cannot be sufficiently depended on as one term substitution, which will be counted according to this metric as a single error, may render the whole sentence invalid, particularly in legal translation. This paper offers a baseline for the quality assessment of machine translation output through post-editing based on Human Translation Edit Rate metric and how its results should be analysed within Arabic-English legal translation context, which may have implications for similar machine translation output quality assessment contexts.