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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/3dmaisons/af-mnt
공식 코드https://github.com/573phn/rnn_typology
공식 코드On the Difficulty of Translating Free-Order Case-Marking Languages
https://github.com/AI4Bharat/indic-bart
공식 코드IndicBART: A Pre-trained Model for Indic Natural Language Generation
https://github.com/AkariAsai/extractive_rc_by_runtime_mt
공식 코드Multilingual Extractive Reading Comprehension by Runtime Machine Translation
https://github.com/AlexJonesNLP/SentimentMT
공식 코드https://github.com/AlexJonesNLP/alt-bitexts
공식 코드Majority Voting with Bidirectional Pre-translation For Bitext Retrieval
https://github.com/DCSaunders/gender-debias
공식 코드Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem
https://github.com/DCSaunders/tagged-gender-coref
공식 코드Neural Machine Translation Doesn't Translate Gender Coreference Right Unless You Make It
https://github.com/DeepLearnXMU/ABD-NMT
공식 코드Asynchronous Bidirectional Decoding for Neural Machine Translation
https://github.com/DeepLearnXMU/Otem-Utem
공식 코드Otem&Utem: Over- and Under-Translation Evaluation Metric for NMT
https://github.com/DeepLearnXMU/User-Driven-NMT
공식 코드https://github.com/EdinburghNLP/opus-100-corpus
공식 코드Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation
https://github.com/ElanVB/optimal_transformer_depth
공식 코드On Optimal Transformer Depth for Low-Resource Language Translation
https://github.com/Elbria/xling-SemDiv-NMT
공식 코드https://github.com/EricGuo5513/TM2T
공식 코드TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts
https://github.com/Heidelberg-NLP/simple-xamr
공식 코드Translate, then Parse! A strong baseline for Cross-Lingual AMR Parsing
https://github.com/Imagist-Shuo/UNMT-SPR
공식 코드Unsupervised Neural Machine Translation with SMT as Posterior Regularization
https://github.com/JunW15/Monolingual-Attack
공식 코드https://github.com/JunW15/NumberTest
공식 코드As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation
https://github.com/LRLNMT/BibleCorpus
공식 코드https://github.com/LauraMartinus/ukuxhumana
공식 코드https://github.com/LeonCrashCode/Encoder-Decoder-Parser
공식 코드https://github.com/LeslieOverfitting/selective_distillation
공식 코드Selective Knowledge Distillation for Neural Machine Translation
https://github.com/Lindsay125/gbpe
공식 코드Finding Better Subword Segmentation for Neural Machine Translation
https://github.com/M4t1ss/MWE-Tools
공식 코드Paying Attention to Multi-Word Expressions in Neural Machine Translation
https://github.com/M4t1ss/SoftAlignments
공식 코드https://github.com/Mao-KU/JASS
공식 코드JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation
https://github.com/McGill-NLP/latent-translation
공식 코드https://github.com/MinkaiXu/RSL-NMT
공식 코드Reciprocal Supervised Learning Improves Neural Machine Translation
https://github.com/Mlair77/nmt_adequacy
공식 코드Prevent the Language Model from being Overconfident in Neural Machine Translation
https://github.com/NLP2CT/Meta-Curriculum
공식 코드Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation
https://github.com/NLP2CT/norm-nmt
공식 코드Norm-Based Curriculum Learning for Neural Machine Translation
https://github.com/NiuTrans/LaMaTE
공식 코드Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation
https://github.com/OpenNMT/OpenNMT
공식 코드https://github.com/PinjiaHe/StructureInvariantTesting
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.