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Proceedings of the 15th Annual Conference of the European Association for Machine Translation
... Carl et al. [2011], Čulo et al. [2014] and Carl and Schaeffer [2017] have all formulated the hypothesis that this predominance of the MT suggestion/TT might have an influence on the final product through priming or directing effects. Indeed, post-edited texts tend to be more literal, include a high number of typical ST constructions and formal equivalences, and also tend to be closer to the source text than HT [Depraetere, 2010;Čulo et al., 2014;Martikainen and Kübler, 2016]. Furthermore, studies have shown that post-edited texts include more unidiomatic or ungrammatical constructions, especially when post-editors are students [Daems et al., 2017;Schumacher, 2019]. ...
... Furthermore, studies have shown that post-edited texts include more unidiomatic or ungrammatical constructions, especially when post-editors are students [Daems et al., 2017;Schumacher, 2019]. Student translators tend to be more tolerant towards MT output and are often liable to accept sub-optimal translations [Schumacher, 2019;Depraetere, 2010;Carl and Schaeffer, 2017;Casas, 2020). Finally, a study conducted on bilingual revision (an activity comparable to PE in the sense that the reviser, like the post-editor, has the choice of primarily orienting his/her attention toward the source or target, but attention also seems to be mainly focused on the target text during revision) by Ciobanu et al. [2019] showed that revisers produce better quality revisions (especially in terms of correcting accuracy errors) when they listen to the source segment (via speech synthesis) while revising. ...
We conducted an experiment with translation students to assess the influence of two different post-editing (PE) strategies (reading the source segment or the target segment first) on three aspects: PE time, ratio of corrected errors and number of optional modifications per word. Our results showed that the strategy that is adopted has no influence on the PE time or ratio of corrected errors. However, it does have an influence on the number of optional modifications per word. Two other thought-provoking observations emerged from this study: first, the ratio of corrected errors showed that, on average, students correct only half of the MT errors, which underlines the need for PE practice. Second, the time logs of the experiment showed that when students are not forced to read the source segment first, they tend to neglect the source segment and almost do monolingual PE. This experiment provides new insight relevant to PE teaching as well as the designing of PE environments.
In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT.
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