Budowanie społeczności tłumaczy

Following these recommendations supports the creation of a full, multilingual post-editing tool. Good translations are defined through the systemic-functional model of House which aims at a contextual correct translation. Write your own post-editing guide and alter these recommendations to fit your own definitions. In most cases the browser-plugin of languageTool is useful as the proof-reading tool.

Społecznościowa lista kontrolna lokalizacji

Nowe w wersji 3.9.

The Community localization checklist which can be found in the menu of each component can give you guidance to make your localization process easy for community translators.


Zarządzanie terminologią

Post-editing of MT with terminology assignment influences each level of the translation process. The machine translation system can be adapted to the specific vocabulary and style with a continued training or neural fuzzy repair. Import your existing translation memory into weblate or create an initial scope with your basic terminology. In the end the lector should be instructed with additional terminology documents to guarantee a good knowledge and output in the field.

Tłumaczenie maszynowe

The quality of the automatic translation (often measured with the BLEU-score) correlates with editing time [1]. Choose a machine backend which supports the needed languages and domains. Make clear how the translation backend functions and which quality the post-editor has to expect.

Przejrzyj tłumaczenia

The translations should be reviewed by a second person after the post-editing. With an impartial and competent revisor, the two man rule reduces the errors and improves the quality and consistency of the content.

Ustrukturyzowana informacja zwrotna

There are many Kontrole i korekty in Weblate which provide structured feedback on quality of the translations.

Definicja tłumaczenia

In addition to the mentalistic and impact-based definitions which make a strong reduction, the text-based linguistic approach fits best with the implemented translation methods. A well-formulated theory for translation evaluation is House’s systemic-functional model, which focuses on the relation between original and translation. The model assumes that translation is an attempt to keep the semantic, pragmatic, and textual meaning of a text equivalent when crossing from one linguistic code to another.

The degree of quality of a translation is based on the degree of equivalence, the correspondence between the text profile and the text function. Because it cannot be calculated automatically, sufficient information should be collected to enable a uniform human evaluation. The two main parameters of agreement in a corresponding model are the macro-context – i.e. embedding in a larger social and literary context – and the micro-context consisting of field, tenor and mode.


  1. Marina Sanchez-Torron and Philipp Koehn in Machine Translation Quality and Post-Editor Productivity, Figure 1: https://www.cs.jhu.edu/~phi/publications/machine-translation-quality.pdf

  2. Joanna Best und Sylvia Kalina.Übersetzen und Dolmetschen: eine Orientierungs-hilfe. A. Francke Verlag Tübingen und Base, 2002. Möglichkeiten der Übersetzungskritik starting on page number 101

  3. neural fuzzy repair, Bram Bulté and Arda Tezcan in Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural MachineTranslation, 2019 https://aclanthology.org/P19-1175.pdf