What can we learn about the selection mechanism for post-editing?

Post-editing is an increasingly common form of human-machine cooperation for translation. One possible support for the post-editing task is offering several machine outputs to a human translator from which then can choose the most suitable one. This paper investigates the selection process for such method to get a better insight into it so that it can be optimally automatised in future work. Experiments show that only about 70% of the selected sentences are the best ranked ones, and that selection mechanism is tightly related to edit distance. Furthermore, five types of performed edit operations are analysed: correcting word form, reordering, adding missing words, deleting extra words and correcting lexical choice. 1 Motivation and related work Machine translation (MT) has improved considerably in recent years thus gaining recognition in the translation industry. However, machine translation outputs have not yet reached the same quality as human translations. Performing the post-editing has become a common practice for improving machine translation outputs. Therefore, more and more attention is paid to various aspects of postediting, such as (Specia, 2011). Prediction of errors in rule-based system outputs has been investigated in (Valotkaite and Asadullah, 2012) in order to facilitate the post-editing process. Analysis of edit operations has been carried out in (Koponen, 2012) in order to understand discrepances between edit distance and translation quality (i.e. predicted post-editing effort). Our work explores the selection criteria applied by professional translators when several translation outputs of each source sentence are offered for post-editing. The scenario is similar to the one in (He et al., 2010), but our approach goes beyond, since they consider only two outputs (one produced by statistical machine translation system and other by translation memory), they do not examine ranking of these outputs, they have not tested their automatic method by professional translators, and they do not analyse edit distances and the performed edit operations. Our main questions are: • Is the translation output which is best for postediting also the best ranked one? • Is the edit distance of the chosen output lower than edit distances of the other outputs? • Are there some (less) preferred edit operations? and to the best of our knowledge they have not been investigated yet. 2 Experimental setup The translation outputs investigated in this work are produced by German-English, German-French and German-Spanish machine translation systems in both directions. The test sets consist of hree domains: news texts taken from WMT tasks (Callison-Burch et al., 2010), technical documentation extracted from the freely available OpenOffice project (Tiedemann, 2009) and client data owned by project partners. The number of Sharon O’Brien, Michel Simard and Lucia Specia (eds.) Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice, Nice, September 2, 2013, p. 11–17. c ©2013 The Authors. This article is licensed under a Creative Commons 3.0 licence, no derivative works, attribution, CC-BY-ND. News OpenOffice Client Total de-en 1788 418 50