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NMT
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Neural machine translation is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ShannonAI/fast-knn-nmt
공식 코드https://github.com/Simpleple/Verdi
공식 코드Verdi: Quality Estimation and Error Detection for Bilingual Corpora
https://github.com/Smu-Tan/ZS-NMT-Variations
공식 코드Towards a Better Understanding of Variations in Zero-Shot Neural Machine Translation Performance
https://github.com/StillKeepTry/ECM-NMT
공식 코드https://github.com/SunbowLiu/CopyingPenalty
공식 코드On the Copying Behaviors of Pre-Training for Neural Machine Translation
https://github.com/THUNLP-MT/UCE4BT
공식 코드Improving Back-Translation with Uncertainty-based Confidence Estimation
https://github.com/Toadoum/Ngambay-French-Neural-Machine-Translation-sba_fr_v1-
공식 코드https://github.com/UBC-NLP/africaNLP2021
공식 코드Translating the Unseen? Yoruba-English MT in Low-Resource, Morphologically-Unmarked Settings
https://github.com/Unbabel/OpenKiwi
공식 코드https://github.com/UtsaChattopadhyay/MSAI/blob/main/SourceCode%20for%20low%20Resource%20MT.zip
공식 코드https://github.com/XL2248/CPCC
공식 코드Modeling Bilingual Conversational Characteristics for Neural Chat Translation
https://github.com/XuezheMax/flowseq
공식 코드FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow
https://github.com/Yusser96/Exploring-Paracrawl-for-Document-level-Neural-Machine-Translation
공식 코드Exploring Paracrawl for Document-level Neural Machine Translation
https://github.com/YuweiYin/UM4
공식 코드https://github.com/ZNLP/SOTA-MT
공식 코드https://github.com/ZhangShiyue/ChrEn
공식 코드ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization
https://github.com/ZhangShiyue/ChrEnTranslate
공식 코드https://github.com/ZhenYangIACAS/NMT_GAN
공식 코드Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets
https://github.com/ZhenYangIACAS/unsupervised-NMT
공식 코드https://github.com/ZurichNLP/domain-robustness
공식 코드https://github.com/ZurichNLP/sockeye
공식 코드https://github.com/ZurichNLP/understanding-mbr
공식 코드Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation
https://github.com/aalto-speech/dbca
공식 코드https://github.com/alexa/alexa-point-of-view-dataset
공식 코드Converting the Point of View of Messages Spoken to Virtual Assistants
https://github.com/alexandra-chron/relm_unmt
공식 코드Reusing a Pretrained Language Model on Languages with Limited Corpora for Unsupervised NMT
https://github.com/alexis-allemann/OpenNMT-py
공식 코드Optimizing the Training Schedule of Multilingual NMT using Reinforcement Learning
https://github.com/aliaraabi/joint_dropout
공식 코드https://github.com/alimrsn79/da_lr_nmt
공식 코드From Priest to Doctor: Domain Adaptaion for Low-Resource Neural Machine Translation
https://github.com/amazon-research/gendered-profession-translations
공식 코드Faithful Target Attribute Prediction in Neural Machine Translation
https://github.com/amazon-research/idiom-mt
공식 코드Automatic Evaluation and Analysis of Idioms in Neural Machine Translation
https://github.com/amazon-science/m3t-multi-modal-translation-bench
공식 코드M3T: A New Benchmark Dataset for Multi-Modal Document-Level Machine Translation
https://github.com/antot/mt_lit_transformer
공식 코드https://github.com/anzeyimana/kinmt_naacl2024
공식 코드Low-resource neural machine translation with morphological modeling
https://github.com/apeterswu/Depth_Growing_NMT
공식 코드https://github.com/apeterswu/RL4NMT
공식 코드A Study of Reinforcement Learning for Neural Machine Translation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.