Improving Machine Translation of Educational Content via Crowdsourcing

Maximiliana Behnke, Antonio Valerio Miceli Barone, Rico Sennrich, Vilelmini Sosoni, Thanasis Naskos, Eirini Takoulidou, Maria Stasimioti, Menno van Zaanan, Sheila Castilho, Federico Gaspari, Panayota Georgakopoulou, Valia Kordoni, Markus Egg, Katia Lida Kermanidis

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publication11th Edition of the Language Resources and Evaluation Conference
Place of PublicationMiyazaki, Japan
PublisherEuropean Language Resources Association (ELRA)
Number of pages5
ISBN (Electronic)979-10-95546-00-9
Publication statusE-pub ahead of print - 12 May 2018
Event11th Edition of the Language Resources and Evaluation Conference - Miyazaki, Japan
Duration: 7 May 201812 May 2018


Conference11th Edition of the Language Resources and Evaluation Conference
Abbreviated titleLREC 2018
Internet address


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