MinT

課題管理システム: #mint
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MinT（ミンティー：Machine in Translation）は機械翻訳サービスの一種でオープンソースのニューラル機械翻訳モデルに基づきます。 当サービスはウィキメディア財団のインフラ上にホスティングされており、他の組織がリリースしたオープンソース・ライセンスの翻訳モデルと競合しません。 無料の知識エコシステムのインフラにとって公開の機械翻訳サービスは鍵となります。 このページでは当該のインフラをもっと多くの人に使えるように、当サービスを判定しようとするイニシアティブを記録します。

この MinT を試用するには、コンテンツ翻訳や翻訳ウィキのサイト translatewiki.net の各プロジェクトに組み込みを使用するか、直接、テスト例を体験できます。

MinTについて

MinTの設計では訳文を複数の機械翻訳モデルから提供します。 当初は以下のモデルを採用します:

  • NLLB-200。メタの研究チームが手がけた最新モデル No Language Left Behind project です。 当モデルは言語 200 件の翻訳に対応し、その中には他の業者がサポートしていない言語も含まれます。
  • OpusMT（オーパス・エムティー）。ヘルシンキ大学が開発したOPUS (Open Parallel Corpus) projectはフリーライセンスの多言語コンテンツをまとめて翻訳モデルOpusMT 翻訳モデル（オーパスMT）を訓練しています。 誰でもさまざまなプロジェクトに参加してデータをOPUSに提供すると、翻訳の質向上に手軽に貢献できます。 例えば利用者がウィキペディアの記事を訳すときにコンテンツ翻訳拡張機能を使うと、システム側は公開した訳文データを新しいリソースとして回収、同モデルの次のバージョンの翻訳の品質改善に役立てします。 あるいはまたTatoebaを使って訳文を提供すると、利用者が手軽に寄与するもう一つの方法になります。
  • IndicTrans2. The IndicTrans2 project provides translation models to support over 20 Indic languages. These models were developed by AI4Bharat@IIT Madras, a research group at the Indian Institute of Technology, Madras.
  • Softcatalà. Softcatalà is a non-profit organization with the goal to improve the use of Catalan in digital products. As part of the Softcatalà Translation project, translation models used in their translator service to translate 10 languages to and from Catalan have been released.
  • MADLAD-400. MADLAD-400 is a multilingual machine translation model by Google Research that supports 419 languages.

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.

技術的な詳細

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 source code, the API spec, and a test instance.

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.

参加する

フィードバックを提供するには協議ページに投稿してください。 改善計画はPhabricator にあがっていき、改善の案を提示したり問題点を指摘したり、タスクが始まっていたらその進捗チェック、それに関する自分なりの視点を共有してください。 完了した工程の確認もでき、以下にある進捗状況のチェック欄をご参照ください。

翻訳者に対してのMinT

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 is available in the following projects:

  • 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.

  • 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.

Wikipedia読者に対してのMinT

Phabricator で追跡中
タスク T341196

The number of topics and the amount of information a reader can learn about from Wikipedia 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 contribute to community-created content when possible.

At the moment the Language team is working on the design and research aspects of the project to identify the best ways to surface MinT on Wikipedia and the technical explorations for the service to work in this context.

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.

更新情報

2024年2月

2024年1月

2023年12月

2023年11月

2023年10月

  • Launched the Language Identification service to automatically detect in which language is written a given text. The service supports the detection of 201 languages, and anyone can access the API to use the service or read the model card for more details. Machine Learning team completed the last checks after deploying to LiftWing and evaluating that the service can "easily withstand a high amount of traffic".
  • Basic support for rich text translation by supporting transferring of markup to apply styling such as words in bold from the source text into the equivalent ones in the machine translation (which lacks format since translation models operate with plain-text).
  • Completed the process to enable MinT for languages with no Wikipedia yet . Translation models in MinT support 25 languages for which there is no Wikipedia. These can be tested in MinT's test instance for speakers of those languages to assess quality, and ensures that translation tools are well-equipped once wikis are created for those languages (as it has been the case with the recent graduation of Fon Wikipedia out of incubator).
  • Completed the process to enable MinT for closely-related languages based on Community input . For some languages where machine translation is not available, Wikipedia editors have asked to have access to machine translation in Content Translation using a related language instead of having no support at all. With this enablement translators of Gan (gan) Wikipedia will have machine translation based on the traditional script variant of Chinese as a starting point.
  • Analysis of translation activity on 55 languages for which MinT provides machine translation for the first time shows how (a) translations have increased 2X since MinT is available, and (b) deletion rates have not increased. Activity levels for these 55 wikis changed from ~500 translations/month, to 1K+ translations/month after MinT was enabled. For example, a recent peak of 2.15K translations were published in August 2023 when MinT was available for those languages, which is a significant increase from 225 translations in August 2022 when MinT was not available for them.
  • Better visibility of translation quality by including a tag in translations where unedited machine translation is close to the limits. This will facilitate analysis about translation quality and limits.

2023年9月

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).

2023年8月

2023年7月

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