機械翻訳
ある言語の文を別の言語へ置き換える
本ページの本文は英語で提供されています。タイトルと導入は日本語化されています。
この能力とは何か
Takes source-language text and outputs a translation. Unlike open-ended generation it has an objective reference — whether the translation is faithful — and unlike summarisation it should not drop content but rebuild it as evenly as possible. Beyond prose it must handle terminology lists, markup and placeholders so the output can be pasted back into the original system.
技術的にどう実現するか
When the Transformer was introduced in 2017, the encoder–decoder with attention was designed for translation, and neural machine translation has since largely replaced statistical systems. Source sentences are encoded and target sentences decoded, trained on parallel corpora. Recent multilingual models cover a hundred-plus languages with one set of parameters and treat translation as an instruction, while low-resource languages lean on transfer from high-resource ones and back-translation.
代表的な製品
6DeepL Translator
2017訳文の質を売りにするニューラル機械翻訳サービス
GPT-4o
2024ネイティブにマルチモーダルな汎用モデル。テキスト・画像・音声をひとつの入口で扱う
Gemini
2023ネイティブにマルチモーダルで、超長文脈を扱う汎用モデル
Qwen
2023多様な規模とマルチモーダル版を備えたオープンウェイトのモデル群
Hunyuan
2023開放ウェイト版を含むテンセントの汎用モデル群
Whisper
2022多言語の音声を文字起こしし、英語へ翻訳する
関連する組織
代表的な用途
- Cross-border documents and contracts
- Localisation of software and websites
- Real-time cross-language communication
- Paper abstracts and subtitles
どう評価するか
- BLEU
- N-gram overlap with references; higher is better but not readability
- COMET
- A neural faithfulness score for translations
- chrF
- Character-level F-score, steadier for morphologically rich languages
限界と難しさ
- Quality on low-resource languages and dialects trails high-resource ones by a wide margin
- Terminology and proper-noun consistency drifts across a long document
- Idioms, puns and culture-specific wording are often translated literally and lose meaning