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We present a parallel wikified data set of parallel texts in eleven language pairs from the educational domain. English sentences are lined up to sentences in eleven other languages (BG, CS, DE, EL, HR, IT, NL, PL, PT, RU, ZH) where names and noun phrases (entities) are manually annotated and linked to their respective Wikipedia pages. For every linked entity in English, the corresponding term or phrase in the target language is also marked and linked to its Wikipedia page in that language. The annotation process was performed via crowdsourcing. In this paper we present the task, annotation process, the encountered difficulties with crowdsourcing for complex annotation, and the data set in more detail. We demonstrate the usage of the data set for Wikification evaluation. This data set is valuable as it constitutes a rich resource consisting of annotated data of English text linked to translations in eleven languages including several languages such as Bulgarian and Greek for which not many LT resources are available.

The limited availability of in-domain training data is a major issue in the training of application-specific neural machine translation models. Professional outsourcing of bilingual data collections is costly and often not feasible. In this paper we analyze the influence of using crowdsourcing as a scalable way to obtain translations of target in-domain data having in mind that the translations can be of a lower quality. We apply crowdsourcing with carefully designed quality controls to create parallel corpora for the educational domain by collecting translations of texts from MOOCs from English to eleven languages, which we then use to fine-tune neural machine translation models previously trained on general-domain data. The results from our research indicate that crowdsourced data collected with proper quality controls consistently yields performance gains over general-domain baseline systems, and systems fine-tuned with pre-existing in-domain corpora.

This paper reports on a comparative evaluation of phrase-based statistical machine translation (PBSMT) and neural machine translation (NMT) for four language pairs, using the PET interface to compare educational domain output from both systems using a variety of metrics, including automatic evaluation as well as human rankings of adequacy and fluency, error-type markup, and post-editing (technical and temporal) effort, performed by professional translators. Our results show a preference for NMT in side-by-side ranking for all language pairs, texts, and segment lengths. In addition, perceived fluency is improved and annotated errors are fewer in the NMT output. Results are mixed for perceived adequacy and for errors of omission, addition, and mistranslation. Despite far fewer segments requiring post-editing, document-level post-editing performance was not found to have significantly improved in NMT compared to PBSMT. This evaluation was conducted as part of the TraMOOC project, which aims to create a replicable semi-automated methodology for high-quality machine translation of educational data.