This page is a translated version of the page MinT and the translation is 15% complete.

MinT (englisch Machine in Translation) ist ein maschineller Übersetzungsdienst, der auf Open-Source-Übersetzungsmodellen basiert. Der Service wird in der Infrastruktur der Wikimedia Foundation gehostet und führt Übersetzungsmodelle aus, die von anderen Organisationen mit einer Open-Source-Lizenz veröffentlicht wurden. Ein freier Übersetzungsdienst kann ein zentraler Bestandteil der grundlegenden Infrastruktur des Ökosystems des Freien Wissens sein. Diese Seite enthält Initiativen zur Ausweitung des Dienstes und zur weiteren Bereitstellung dieser Infrastruktur.

Du kannst MinT als Teil von Projekten wie der Übersetzung von Inhalten und translatewiki.net, oder direkt in einer Testinstanz ausprobieren.

Überblick über die MinT-Initiativen

Maschinelle Übersetzung kann in verschiedenen Kontexten nützlich sein. Da weitere Angebote MinT für verschiedene Zwecke verwenden, ist es nützlich, diese unterschiedlichen Kontexte zu unterscheiden. Auf diese Weise ist es für Benutzer, die einen Fehler melden, klarer, wo er behoben werden muss.

  • MinT Service. Der Backend-Dienst, der Open-Source-

Neural-Maschinenübersetzungsmodelle betreibt.

    • MinT test instance. Eine grundlegende Oberfläche, um die verschiedenen Übersetzungsmodelle auszuprobieren.
  • MinT for Translators. Initiative zur Integration des MinT-Dienstes in Tools, die andere maschinelle Übersetzungsdienste wie die Inhaltsübersetzung und die Übersetzungsverlängerung unterstützen.
    • MinT Client for Content Translation. Client exposing the MinT Service as one of the machine translation services available in Content Translation.
    • MinT Client for Translate extension. Client exposing the MinT Service as one of the machine translation services available in the Translate extension.
  • MinT for Wiki Readers. Product to enable readers to use machine translation to read contents from other languages on a wiki.

You can read more below about each of the MinT initiatives.

Beteilige dich

Gib uns gerne Feedback auf der Diskussionsseite. Planned improvements are captured in Phabricator (more info), you can report wrong behavior or propose feature enhancements, track the progress of any task, and share your perspective on it. For completed work you can also check the status updates below.

MinT Service

The MinT Service is designed to provide translations from multiple machine translation models. Currently, it uses the following models:

MinT supports over 200 languages, with more than 70 languages not supported by other services (including 27 languages for which there is no Wikipedia yet). You can read more about the initial release of MinT and check some frequently asked questions in the summary page for the service.

Technische Details

The translation models have been optimized for performance using OpenNMT Ctranslate2 library in order to avoid the need for GPU acceleration. This makes it easier for organizations and individuals to build and run their own instances. For more details you can check the following:

MinT provides a platform to run multiple translation models. In order to support different initiatives, aspects such as sentence segmentation, language detection, pre/post-processing of contents, and rich format support has been developed on top of the plain-text based models.

Test instance

The MinT test instance is a basic interface to try the different translation models. It allow to translate contents across the selected language pairs and select the preferred translation model when multiple are available. This allows different communities to check how well the models support their language. This instance is intended for testing, so performance and availability may be reduced compared to other MinT-based products. You can check the availability status of the MinT test instance.

MinT für Übersetzer

Mobile translation using MinT

Translation is a common way to contribute in the Wikimedia ecosystem for multilingual users. Machine translation can provide a useful initial translation for users to review and improve. The Language team has developed tools to support translations in their workflows that can integrate different machine translation services to speed up their processes. Once MinT was available, integrating it with these tools was a logical next step to amplify their impact. MinT ist in folgenden Projekten verfügbar:

  • Content Translation. Content Translation provides guidance to create a translation of a Wikipedia article into another language.

Content Translation integrates several translation services to provide an initial translation. You can check which languages supported by MinT are available in Content Translation

  • Localization infrastructure. The Translate extension provides the infrastructure used to translate our software and multilingual pages.

Communities of translators use it on translatewiki.net , Wikimedia Meta-wiki, MediaWiki.org and more.


MinT für Wiki-Leser

The number of topics and the amount of information a reader can learn about from Wikipedia and other wikis depends on the languages they speak. Machine translation can help people to learn more about their topics of interest when the content is not available in their language.

This initiative explores how to surface the machine translation support from MinT in Wikipedia articles in a way that:

  • Allows readers to learn more about the topics of interest from other languages.
  • Clearly differentiates automatically generated content from community-created one.
  • Encourages to access and contribute to community-created content when possible.

At the moment the Language team is working on the initial implementations for this initiative based on the research and the designs. Learnings based on data and community input will determine the next steps for the initiative.

MinT more widely available

Working on the previous initiatives will help to polish and solidify the system. For now, the MinT API is only available for Wikimedia products. As the system gets ready, we'll consider a wider exposure. Providing a service that can be used by communities in innovative ways can be a very powerful tool. New initiatives to make MinT more widely available will be captured here in the future. Meanwhile, feel free to configure your own MinT instance to experiment with it.

Disclaimer

  1. Accuracy of MinT’s Translations - The accuracy of translations generated by MinT may vary. Translations may not be entirely accurate or may not always convey the intended meaning or context of the original content. Wikimedia makes no representations or warranties regarding the accuracy or adequacy of the automatically translated content.
  2. Limitation of Liability - Wikimedia, its affiliates, and employees are not liable for any direct, indirect, incidental, punitive, or consequential damages, including but not limited to damages for goodwill, use, data, or any other intangible losses arising out of or in connection with the use of MinT or translations generated with MinT.
  3. Creative Commons Compliance - Translations generated with MinT are considered derivative works under the applicable Creative Commons license governing the original content. Users shall comply with the terms of the applicable Creative Commons license when using translated content.
  4. Terms of Use and Privacy Policy - Use of MinT is subject to Wikimedia's Terms of Use and Privacy Policy.

Updates

Februar 2024

Januar 2024

Dezember 2023

November 2023

Oktober 2023

September 2023

A message well received by the attendees.

  • Research planning started with an initial draft of the research brief for MinT on Wikipedia
  • Continuing technical explorations for applying machine translation beyond plain text (what underlying models provide) to support the Wikipedia context: A new improved approach for sentence segmentation (with a demo page to try) that provides a more accurate way to identify when a sentence ends in different languages, and with a preference to avoid splitting in case of doubt (preferred in the context of machine translation to avoid fragmenting the context of a translation, for example, misinterpreting the dot of an abbreviation as a fullstop).

August 2023

Juli 2023