Toward Crowdsourcing Translation Post-editing: A Thematic Systematic Review

Crowdsourcing Translation as a Post-Editing Method (CTPE) has emerged as a rapid and inexpensive method for translation and has drawn significant attention in recent years. This qualitative study aims to analyze and synthesize the approaches and aspects underpinning CTPE research and to identify its potential that is yet to be discovered. Through a systematic literature review focused on empirical papers, we examined the limited literature thematically and identified recurring central themes. Our review reveals that the topic of CTPE requires further attention and that its potential benefits are yet to be fully discovered. We discuss the eight core concepts that emerged during our analysis, including the purpose of CTPE, CTPE areas of application, ongoing CTPE processes, platform and crowd characteristics, motivation, CTPE domains, and future perspectives. By highlighting the strengths of CTPE, we conclude that it has the potential to be a highly effective translation method in various domains

Machine-assisted translation by Human-in-the-loop Crowdsourcing for Bambara

Language is more than a tool of conveying information; it is utilized in all aspects of our lives. Yet only a small number of languages in the 7,000 languages worldwide are highly resourced by human language technologies (HLT). Despite African languages representing over 2,000 languages, only a few African languages are highly resourced, for which there exists a considerable amount of parallel digital data. We present a novel approach to machine translation (MT) for under-resourced languages by improving the quality of the model using a paradigm called ``humans in the Loop.\u27\u27 This thesis describes the work carried out to create a Bambara-French MT system including data discovery, data preparation, model hyper-parameter tuning, the development of a crowdsourcing platform for humans in the loop, vocabulary sizing, and segmentation. We present a novel approach to machine translation (MT) for under-resourced languages by improving the quality of the model using a paradigm called ``humans in the Loop.\u27\u27 We achieved a BLEU (bilingual evaluation understudy) score of 17.5. The results confirm that MT for Bambara, despite our small data set, is viable. This work has the potential to contribute to the reduction of language barriers between the people of Sub-Saharan Africa and the rest of the world

Community post-editing of machine-translated user-generated content

With the constant growth of user-generated content (UGC) online, the demand for quick translations of large volumes of texts increases. This is often met with a combination of machine translation (MT) and post-editing (PE). Despite extensive research in post-editing with professional translators or translation students, there are few PE studies with lay post-editors, such as domain experts. This thesis explores lay post-editing as a feasible solution for UGC in a technology support forum, machine translated from English into German. This context of lay post-editing in an online community prompts for a redefinition of quality. We adopt a mixed-methods approach, investigating PE quality quantitatively with an error annotation, a domain specialist evaluation and an end-user evaluation. We further explore post-editing behaviour, i.e. specific edits performed, from a qualitative perspective. With the involvement of community members, the need for a PE competence model becomes even more pressing. We investigate whether Gopferich’s translation competence (TC) model (2009) may serve as a basis for lay post-editing. Our quantitative data proves with statistical significance that lay post-editing is a feasible concept, producing variable output, however. On a qualitative level, post-editing is successful for short segments requiring ~35% post-editing effort. No post-editing patterns were detected for segments requiring more PE effort. Lastly, our data suggests that PE quality is largely independent of the profile characteristics measured. This thesis constitutes an important advance in lay post-editing and benchmarking the evaluation of its output, uncovering difficulties in pinpointing reasons for variance in the resulting quality

Low-Resource Unsupervised NMT:Diagnosing the Problem and Providing a Linguistically Motivated Solution

Unsupervised Machine Translation hasbeen advancing our ability to translatewithout parallel data, but state-of-the-artmethods assume an abundance of mono-lingual data. This paper investigates thescenario where monolingual data is lim-ited as well, finding that current unsuper-vised methods suffer in performance un-der this stricter setting. We find that theperformance loss originates from the poorquality of the pretrained monolingual em-beddings, and we propose using linguis-tic information in the embedding train-ing scheme. To support this, we look attwo linguistic features that may help im-prove alignment quality: dependency in-formation and sub-word information. Us-ing dependency-based embeddings resultsin a complementary word representationwhich offers a boost in performance ofaround 1.5 BLEU points compared to stan-dardWORD2VECwhen monolingual datais limited to 1 million sentences per lan-guage. We also find that the inclusion ofsub-word information is crucial to improv-ing the quality of the embedding

Translation and Social Media Communication in the Age of the Pandemic

This collection of essays represents the first of its kind in exploring the conjunction of translation and social media communication, with a focus on how these practices intersect and transform each other against the backdrop of the cascading COVID-19 crisis. The contributions in the book offer empirical case studies as well as personal reflections on the topic, illuminating a broad range of themes such as knowledge translation, crisis communications, language policies, cyberpolitics and digital platformization. Together they demonstrate the vital role of translation in the trust-based construction of global public health discourses, while accounting for the new medialities that are reshaping the conception, experience and critique of translation in response to the cultural, political and ecological challenges in the post-pandemic world. Written by leading scholars in translation studies, media studies and literary studies, this volume sets to open up new conversations among these fields in relation to the global pandemic and its aftermath

Translation and Social Media Communication in the Age of the Pandemic

This collection of essays represents the first of its kind in exploring the conjunction of translation and social media communication, with a focus on how these practices intersect and transform each other against the backdrop of the cascading COVID-19 crisis. The contributions in the book offer empirical case studies as well as personal reflections on the topic, illuminating a broad range of themes such as knowledge translation, crisis communications, language policies, cyberpolitics and digital platformization. Together they demonstrate the vital role of translation in the trust-based construction of global public health discourses, while accounting for the new medialities that are reshaping the conception, experience and critique of translation in response to the cultural, political and ecological challenges in the post-pandemic world. Written by leading scholars in translation studies, media studies and literary studies, this volume sets to open up new conversations among these fields in relation to the global pandemic and its aftermath