构建翻译者社区

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.

社区本地化检查清单

3.9 新版功能.

可以在每个部件的菜单中找到 社区本地化检查清单,它可以就如何让社区翻译者轻松进行本地化过程为您提供指导。

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专业术语管理

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.

机器翻译

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.

复查翻译

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.

结构化的反馈

There are many 检查和修正 in Weblate which provide structured feedback on quality of the translations.

翻译定义

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.

原文

  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. post-editing guide, Sharon O’Brien and Fred Hollowood in MT Post-editing Guidelines, 2010 https://www.taus.net/academy/best-practices/postedit-best-practices/machine-translation-post-editing-guidelines

  4. 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