Building a translators community

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 a proof-reading tool.

Many times translators will find problems with the source strings. Make sure it is easy for them to report such problems. To gather this feedback, you can set up the 儲存庫瀏覽器 field on your Weblate component, for translators to propose their changes to the upstream repository. You can also receive translator comments if you setup 來源字串漏洞報告位址.

組件診斷

Guidance is shown in the Diagnostics tab on each component. These alerts are dismissible and point to the configuration or documentation needed to make your localization process easier for community translators.

專有名詞管理

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.

機器翻譯

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.

檢閱翻譯

The translations should be reviewed by a second person after the post-editing. With an impartial and competent reviewer, the two people rule reduces the errors and improves the quality and consistency of the content. Providing reviewers with previews or alpha translations will make for the best review. Screenshots, explanations also help to review the strings in context.

結構化回饋

Weblate 中有許多 檢查與修復 為翻譯品質提供結構化回饋，並在翻譯期間給予視覺提示。這些功能有助於避免重複發生的錯誤，並幫助翻譯者理解程式碼的工作方式。

翻譯定義

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.

來源

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 Orientierungshilfe. 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 Machine Translation, 2019 https://aclanthology.org/P19-1175.pdf