Help:Content translation/Translating/Initial machine translation/ja

翻訳に新しい段落を加えるには、ゼロから書き起こすか、手始めに機械翻訳にかけるかします. 機械翻訳が認められている場合、既定で訳文の初稿作りに使います. 異なる選択肢とそれぞれが使えるかどうか、また機械翻訳利用における注意点は下記の説明のとおりです.

訳文初稿の制限
ツール欄の"翻訳の初稿"オプションは、段落単位で作業の出発点として最初にどのコンテンツを使うか選びます. 選択肢は以下のとおりです.


 * Use a machine translation service. This allows you to start with an automatically translated version of the original paragraph. The number and name of these options will vary. Options such as "Use Apertium" or "Use Yandex" will be available depending on the supported languages for these services (more on this on the next section).
 * Copy original content. The original paragraph will be copied over into the translation. Although content will remain in the original language, some elements are adapted to the target wiki. for example, links will point to the corresponding article in the target language, and templates will be converted into the equivalent ones. Translators still have to rewrite the content completely, but the adapted elements may be easier to reuse.
 * Start with an empty paragraph. Starting with an empty paragraph can be useful in cases where the alternative content requires more work than just typing it.

You can quickly switch between the different approaches independently on each paragraph, since each one may work best on different kinds of content. Switching between the different approaches preserves the changes you made on the paragraph. In this way, you can try a different approach even if you started editing the original one without the fear to lose your changes if you finally decide to go back to the original approach. Two additional options are relevant in this context:


 * The reset translation option is available when you made modifications on the initial content provided. It allows to restore the initial content by discarding the changes you made.
 * The mark as default option is available when you select an approach for a paragraph that is not the default. It allows to set the default approach for the next paragraphs that are added to the translation. This can be very convenient if you found that a particular translation service works generally better than the default one.

Machine translation availability
Content translation integrates several translation services, and each service support a different set of languages. The services supported are listed below with a link to the list of languages they support:


 * Apertium (対応している言語)
 * OpusMT ( languages supported)
 * LingoCloud (対応している言語)
 * Matxin (対応している言語)
 * Google Translate (languages supported)
 * Yandex (対応している言語)
 * Youdao (対応している言語)

The list of languages above point to the configuration code to make sure that the information is in sync with the way the tool currently works. The list show the language code for the source language at the initial indentation level and the codes of all the supported target languages below it.

Language enablement is done in a gradual way based on the observed results and the community feedback. It is possible that machine translation has not been enabled yet, even if they are supported by the underlying services.

Improving existing translation services
When you publish a translation with Content Translation you are already helping translation services to improve. All the corrections you make to the initial machine translations are exposed through an API and data dumps and can be potentially used to improve existing services.

In addition to that, some of the translation services listed above provide specific ways you can contribute to their projects.

OpusMT
OpusMT is an open source neural machine translation system that is trained with multilingual documents freely licensed available online. This open corpus is used to train the translation system, and expanding the corpus will lead to better translations. The contents generated by using Content Translation are integrated automatically in the corpus, but you can contribute to expand the corpus further:


 * Propose new sources to be integrated in the open corpus. You can contact Jörg Tiedemann to propose a new data source to expand the corpus.
 * Submit documents directly (still a preliminary prototype). You can upload translated documents in various formats including translated webpages to be incorporated to the corpus.

OpusMT is based on MarianNMT which is also an open source project. People with technical knowledge and interested in machine learning can also contribute to improve it.

Apertium
Apertium is an open source rule-based translation system. You can contribute to the project by encoding the language rules of your language. This process requires both linguistic and advanced technical knowledge, but you can get support from the Apertium team to expand the translation support for a new language pair.

Google Translate
Google Translate is not an open source project, but there are still ways for users to contribute back:


 * Join the Google Translate Community to provide translations that help train their system.
 * Report bugs when the translation system shows unexpected behavior when dealing with certain elements such as spacing or end of sentence marks (view full list).

Expanding the language support with new translation services
Content translation has been designed as an extensible platform. So it is possible to develop new clients to integrate additional translation services. Some considerations about the way translation services are integrated:


 * Machine translations and the user corrections made are publicly as part of the data on published translations, which can provide a useful resource to create or improve your translation service.
 * External services integrated only receive publicly available wiki content, and return a translated version of such content that is compatible with the licenses used in the wiki. No personal information is shared with the translation services.

Feedback on the support provided for each language is very useful. Please, let us know if you are missing support for some language, or whether higher quality options are available for it. You can provide such feedback on the project talk page or in this ticket.

Considerations on machine translation
Machine translation is far from perfect when intended as a final outcome. However, many users find it very useful as a starting point. Please make sure to review the content from these different perspectives:


 * Make sure the original meaning is preserved.
 * Check that there is no information missing, especially for elements such as links, references and templates that include information that is not always visible on the surface.
 * Read the translated content to make sure it reads natural as an independent page.

要素が複雑な場合の限界
予測と異なる翻訳が出力される場合があります.


 * Some of the services supported only work with plain text. This means that formatting and rich content elements such as links and citations from the original article are lost in the translation, and Content translation needs to guess where those belong in the translated text. Re-adding those elements is not always perfect and some elements may be in the wrong position or applied to the wrong part of the text.
 * Complex elements such as references or templates may use a different structure in each language, which makes it hard to transfer the content from one language into the other. Make sure to review the contents inside those elements to make sure there is no important information missing.

機械翻訳の出力を査読するよう促す
自動的に翻訳の初稿を査読するように促す仕組みが複数あります. これらのツールは、自動翻訳を使った初稿が必ず十分に査読を受けてから、その後に公開されるように確認します.