BLEU score: 30.4
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Machine Translation
Machine translation is the task of translating a sentence in a source language to a different target language.
Approaches for machine translation can range from rule-based to statistical to neural-based. More recently, encoder-decoder attention-based architectures like BERT have attained major improvements in machine translation.
One of the most popular datasets used to benchmark machine translation systems is the WMT family of datasets. Some of the most commonly used evaluation metrics for machine translation systems include BLEU, METEOR, NIST, and others.
<span style="color:grey; opacity: 0.6">( Image credit: Google seq2seq )</span>
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Dataset기록된 모델 예시MetricPaperCode
데이터셋
101개논문
BLEU score: 29.98
BLEU score: 32.93
BLEU score: 29.56
BLEU score: 32.31
BLEU score: 32.43
BLEU score: 33.97
BLEU: 43.1
BLEU score: 34.18
BLEU score: 36.20
BLEU: 34.1
BLEU score: 30.08
라이브러리
Papers with Code 리더보드 row에 연결된 구현 저장소입니다.
하위 태스크
UM
Unsupervised Machine Translation
MM
Multimodal Machine Translation
LN
Low-Resource Neural Machine Translation
AP
Automatic Post-Editing
BL
Bilingual Lexicon Induction
CL
Clinical Language Translation
LD
Legal Document Translation
LR
Low Resource NMT
LR
Low Resource Neural Machine Translation
T
Transliteration