기계 번역
한 언어의 글을 다른 언어로 옮긴다
이 페이지의 본문은 영어로 제공됩니다. 제목과 요약은 한국어로 번역되었습니다.
이 능력이 뜻하는 것
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.
기술적으로 구현하는 방법
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.
대표 제품
6DeepL Translator
2017번역 품질을 내세우는 신경망 기계 번역 서비스
GPT-4o
2024네이티브 멀티모달 범용 모델. 텍스트·이미지·오디오를 한 창구에서 다룬다
Gemini
2023네이티브 멀티모달에 초장문 문맥을 다루는 범용 모델
Qwen
2023여러 규모와 멀티모달 버전을 아우르는 오픈웨이트 모델 계열
Hunyuan
2023오픈웨이트 버전을 포함한 텐센트의 범용 모델 계열
Whisper
2022여러 언어의 음성을 받아쓰고 영어로 번역한다
관련 기관
대표적 용도
- Cross-border documents and contracts
- Localisation of software and websites
- Real-time cross-language communication
- Paper abstracts and subtitles
성능을 평가하는 방법
- 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
경계와 난점
- 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