Dataset Information

Using Machine Translation and Post-Editing in the TRAPD Approach: Effects on the Quality of Translated Survey Texts.

ABSTRACT: A highly controlled experimental setting using a sample of questions from the European Social Survey (ESS) and European Values Study (EVS) was used to test the effects of integrating machine translation and post-editing into the Translation, Review, Adjudication, Pretesting, and Documentation (TRAPD) approach in survey translation. Four experiments were conducted in total, two concerning the language pair English-German and two in the language pair English-Russian. The overall results of this study are positive for integrating machine translation and post-editing into the TRAPD process, when translating survey questionnaires. The experiments show evidence that in German and Russian languages and for a sample of ESS and EVS survey questions, the effect of integrating machine translation and post-editing on the quality of the review outputs-with quality understood as texts output with the fewest errors possible-can hardly be distinguished from the quality that derives from the setting with human translations only.

SUBMITTER: Zavala-Rojas D

PROVIDER: S-EPMC11014682 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

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Using Machine Translation and Post-Editing in the TRAPD Approach: Effects on the Quality of Translated Survey Texts.

Zavala-Rojas Diana D Behr Dorothée D Dorer Brita B Sorato Danielly D Keck Veronika V

Public opinion quarterly 20240328 1

A highly controlled experimental setting using a sample of questions from the European Social Survey (ESS) and European Values Study (EVS) was used to test the effects of integrating machine translation and post-editing into the Translation, Review, Adjudication, Pretesting, and Documentation (TRAPD) approach in survey translation. Four experiments were conducted in total, two concerning the language pair English-German and two in the language pair English-Russian. The overall results of this st ...[more]

PMID: 38617051

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Project description:ObjectiveTo analyze techniques for machine translation of electronic health records (EHRs) between long distance languages, using Basque and Spanish as a reference. We studied distinct configurations of neural machine translation systems and used different methods to overcome the lack of a bilingual corpus of clinical texts or health records in Basque and Spanish.Materials and methodsWe trained recurrent neural networks on an out-of-domain corpus with different hyperparameter values. Subsequently, we used the optimal configuration to evaluate machine translation of EHR templates between Basque and Spanish, using manual translations of the Basque templates into Spanish as a standard. We successively added to the training corpus clinical resources, including a Spanish-Basque dictionary derived from resources built for the machine translation of the Spanish edition of SNOMED CT into Basque, artificial sentences in Spanish and Basque derived from frequently occurring relationships in SNOMED CT, and Spanish monolingual EHRs. Apart from calculating bilingual evaluation understudy (BLEU) values, we tested the performance in the clinical domain by human evaluation.ResultsWe achieved slight improvements from our reference system by tuning some hyperparameters using an out-of-domain bilingual corpus, obtaining 10.67 BLEU points for Basque-to-Spanish clinical domain translation. The inclusion of clinical terminology in Spanish and Basque and the application of the back-translation technique on monolingual EHRs significantly improved the performance, obtaining 21.59 BLEU points. This was confirmed by the human evaluation performed by 2 clinicians, ranking our machine translations close to the human translations.DiscussionWe showed that, even after optimizing the hyperparameters out-of-domain, the inclusion of available resources from the clinical domain and applied methods were beneficial for the described objective, managing to obtain adequate translations of EHR templates.ConclusionWe have developed a system which is able to properly translate health record templates from Basque to Spanish without making use of any bilingual corpus of clinical texts or health records.

Dataset Information

Using Machine Translation and Post-Editing in the TRAPD Approach: Effects on the Quality of Translated Survey Texts.

Publications

Using Machine Translation and Post-Editing in the TRAPD Approach: Effects on the Quality of Translated Survey Texts.

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