Humans in the Loop: An investigation into the impact of the quality of source texts on the work of post-editors and translators at TranslateMedia

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TranslateMedia is a leading translation company that specialises in the translation of advertising and marketing materials. The company is implementing post-editing of adaptive neural machine translation (ANMT), i.e. human correction of machine translation (MT) output, as part of its professional workflow. Non-canonical and poorly written source texts are a huge source of errors for MT, because flawed input will normally generate flawed output (Geiger et al., 2019), constituting a major challenge for the effective integration of MT into the translation workflow. This research will examine the impact of the quality of source texts on MT output, post-editing effort, translators' productivity and professional practices.Research on post-editing of neural machine translation (NMT) is still scarce (Jia et al., 2019) and no previous research has focused on its use for the translation of advertising and marketing materials, nor on the impact of the quality of source texts on MT output and on the work of post-editors. Moreover, the integration of MT into the professional workflow involves a significant change in translation practices, therefore human and organisational aspects play a key role on whether the new process will be successful (Olohan, 2011). However, Translation Studies research has paid relatively little attention to the social aspect of translation technology and its interaction with translation practices (Olohan, 2011; 2017; 2020).How does the quality of source texts affect the work of post-editors and translators at TranslateMedia, using adaptive neural machine translation and full human post-editing to translate advertising and marketing materials? - To what extent do poorly written and non-canonical source texts affect the quality of MT output? - How does the quality of source texts affect post-editing practices? - What impact do poorly written and non-canonical source texts have on the productivity and quality of work of post-editors? - How do low-quality and non-standard source texts affect the correlation between estimated, real and perceived post-editing effort? Answering these questions will make a significant contribution to our understanding of how latest developments in MT technology are changing professional translation workflows and practices. Some of the most interesting aspects that this project will aim to shed light on include (a) how low-quality source texts affect linguists' productivity, (b) whether translators change their professional practices when post-editing MT output from low-quality source texts, and (c) whether translators' perceptions of post-editing effort under these circumstances correspond to the real post-editing effort.This research will investigate the impact of the quality of English source texts on the MT output into TranslateMedia's most requested European target languages, i.e. French, German, Italian, Spanish, Portuguese and Dutch. TranslateMedia will provide access to datasets and resources, that will be used to create a corpus of non-canonical and poorly written source texts, and a corpus of comparable size and linguistic domain, but more linguistically standard. Automatic estimations of MT quality for both corpora will be compared to results from human MT quality assessment, which will be undertaken by TranslateMedia's freelance translators. Questionnaires and semi-structured interviews will be used to find out whether the quality of MT input affects post-editing practices and what are the implications on translators' productivity. For example, they will include questions on whether the quality of the MT output affects translators' attitude towards the post-editing task, their satisfaction with the quality of their work and with their professional role. They will also include questions on perceived post-editing effort, which will be compared to automatic estimations and the actual (measured) post-editing effort.