Improving machine translation of educational content via crowdsourcing
| Autor: | Behnke, M., Barone, A. V. M., Sennrich, R., Sosoni, V., Naskos, T., Takoulidou, E., Stasimioti, M., Menno van Zaanen, Castilho, S., Gaspari, F., Georgakopoulou, P., Kordoni, V., Egg, M., Kermanidis, K. L. |
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| Přispěvatelé: | Nicoletta Nicoletta, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Koiti Hasida, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis, Takenobu Tokunaga, Maximiliana, Behnke, Antonio Valerio Miceli, Barone, Rico, Sennrich, Vilelmini, Sosoni, Thanasis, Nasko, Eirini, Takoulidou, Maria, Stasimioti, Menno van, Zaanen, Sheila, Castilho, Gaspari, F, Panayota, Georgakopoulou, Valia, Kordoni, Markus, Egg, Katia Lida, Kermanidis, McCrae, John P., Chiarcos, Christian, Declerck, Thierry, Gracia, Jorge, Klimek, Bettina |
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| Zdroj: | Scopus-Elsevier Behnke, Maximiliana, Miceli Barone, Antonio Valerio, Sennrich, Rico, Sosoni, Vilelmini, Naskos, Thanasis, Takoulidou, Eirini, Stasimioti, Maria, Menno, van Zaanen, Castilho, Sheila ORCID: 0000-0002-8416-6555 Behnke, M, Miceli Barone, A V, Sennrich, R, Sosoni, V, Naskos, T, Takoulidou, E, Stasimioti, M, van Zaanan, M, Castilho, S, Gaspari, F, Georgakopoulou, P, Kordoni, V, Egg, M & Kermanidis, K L 2018, Improving Machine Translation of Educational Content via Crowdsourcing . in 11th Edition of the Language Resources and Evaluation Conference . Miyazaki, Japan, pp. 3343-3347, 11th Edition of the Language Resources and Evaluation Conference, Miyazaki, Japan, 7/05/18 . < http://www.lrec-conf.org/proceedings/lrec2018/summaries/855.html > |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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