https://github.com/mosharafhossain/negation-mt
공식 코드It's not a Non-Issue: Negation as a Source of Error in Machine Translation
Papers with Code 아카이브
Machine Translation
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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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mounicam/bisect
공식 코드BiSECT: Learning to Split and Rephrase Sentences with Bitexts
https://github.com/mp2893/med2vec
공식 코드https://github.com/mraihan-gmu/mHumanEval-Benchmark
공식 코드mHumanEval -- A Multilingual Benchmark to Evaluate Large Language Models for Code Generation
https://github.com/mriosb08/medical-nmt-haitrans
공식 코드Impact of Domain-Adapted Multilingual Neural Machine Translation in the Medical Domain
https://github.com/ms-dot-k/tmt
공식 코드https://github.com/mswellhao/dangle
공식 코드Disentangled Sequence to Sequence Learning for Compositional Generalization
https://github.com/mt-upc/fairseq
공식 코드Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22
https://github.com/mt-upc/logit-explanations
공식 코드Explaining How Transformers Use Context to Build Predictions
https://github.com/mt-upc/resetox
공식 코드ReSeTOX: Re-learning attention weights for toxicity mitigation in machine translation
https://github.com/mt-upc/transformer-contributions-nmt
공식 코드https://github.com/mtresearcher/terminology_dataset
공식 코드Training Neural Machine Translation To Apply Terminology Constraints
https://github.com/mtsomethree/constrdecoding
공식 코드Controllable Text Generation with Neurally-Decomposed Oracle
https://github.com/mttravel/Dictionary-based-MT
공식 코드Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences
https://github.com/multi30k/dataset
공식 코드https://github.com/n-mehandru/physicianqe
공식 코드https://github.com/nadavborenstein/ee-from-historical-ads
공식 코드Multilingual Event Extraction from Historical Newspaper Adverts
https://github.com/naist-nlp/knn-seq
공식 코드https://github.com/namisan/exdeep-nmt
공식 코드https://github.com/naver-ai/MetricMT
공식 코드Reward Optimization for Neural Machine Translation with Learned Metrics
https://github.com/naver/covid19-nmt
공식 코드A Multilingual Neural Machine Translation Model for Biomedical Data
https://github.com/ncsoft/Align-to-Distill
공식 코드https://github.com/ndnlp/parity
공식 코드https://github.com/neelbhandari6/nmt_text_attack
공식 코드Lost In Translation: Generating Adversarial Examples Robust to Round-Trip Translation
https://github.com/netease-youdao/cmlm_da
공식 코드https://github.com/nethermanpro/transvip
공식 코드TransVIP: Speech to Speech Translation System with Voice and Isochrony Preservation
https://github.com/neubig/globalutility
공식 코드Systematic Inequalities in Language Technology Performance across the World's Languages
https://github.com/neubig/rapid-adaptation
공식 코드Rapid Adaptation of Neural Machine Translation to New Languages
https://github.com/neulab/BARTScore
공식 코드https://github.com/neulab/ExplainaBoard
공식 코드https://github.com/neulab/compare-mt
공식 코드compare-mt: A Tool for Holistic Comparison of Language Generation Systems
https://github.com/neulab/contextual-mt
공식 코드Measuring and Increasing Context Usage in Context-Aware Machine Translation
https://github.com/neulab/extreme-adaptation-for-personalized-translation
공식 코드Extreme Adaptation for Personalized Neural Machine Translation
https://github.com/neulab/word-embeddings-for-nmt
공식 코드When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?
https://github.com/neulab/xnmt
공식 코드https://github.com/ngovinhtn/charTransform
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.