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SciBabel: a system for crowd-sourced validation of automatic translations of scientific texts - PubMed

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SciBabel: a system for crowd-sourced validation of automatic translations of scientific texts

Felipe Soares et al. Genomics Inform. 2020 Jun.

Abstract

Scientific research is mostly published in English, regardless of the researcher's nationality. However, this growing practice impairs or hinders the comprehension of professionals who depend on the results of these studies to provide adequate care for their patients. We suggest that machine translation (MT) can be used as a way of providing useful translation for biomedical articles, even though the translation itself may not be fluent. To tackle possible mistranslation that can harm a patient, we resort to crowd-sourced validation of translations. We developed a prototype of MT validation and edition, where users can vote for that translation as valid, or suggest modifications (i.e., post-editing the MT). A glossary match system is also included, aiming at terminology consistency.

Keywords: PubMed; crowdsourcing; linguistics; machine translation; medical informatics applications.

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Conflict of interest statement

Conflicts of Interest

No potential conflict of interest relevant to this article was reported.

Figures

Fig. 1.
Fig. 1.

Interface for translation evaluation. Users can flag the translation as adequate (i.e., Like) or edit the proposed translation using the links in the Actions column.

Fig. 2.
Fig. 2.

Interface for translation editions Users can edit the proposed translation to make corrections on mistranslations or terminology adequacy. The prototype also shows suggested translations from terms matched in a dictionary, aiming at providing terminology consistency.

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