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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/lkfo415579/MT-Readling-List
https://github.com/lmtoan/nlp-cs224n
Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models
https://github.com/luofuli/DualLanST
https://github.com/luofuli/DualRL
https://github.com/luyige/test-git
Massive Exploration of Neural Machine Translation Architectures
https://github.com/m3yrin/code2seq
code2seq: Generating Sequences from Structured Representations of Code
https://github.com/macco3k/deepstories
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/mahimanzum/seq2seq_corrected_for_NCT
Massive Exploration of Neural Machine Translation Architectures
https://github.com/makcedward/nlpaug
Synthetic and Natural Noise Both Break Neural Machine Translation
https://github.com/microsoft/BlingFire
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/microsoft/deepnmt
https://github.com/midobal/OpenNMT-py
https://github.com/mike-a-yen/date-translation
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/mindspore-ai/models/tree/master/official/nlp/gnmt_v2
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/modelscope/modelscope
Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation
https://github.com/neqkir/attention-mechanism
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/ngovinhtn/JaViCorpus
Combining Advanced Methods in Japanese-Vietnamese Neural Machine Translation
https://github.com/njunlp/knn-box
https://github.com/nkjsy/Neural-Machine-Translation
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/nrc-cnrc/sockeye-multisource
https://github.com/nxphi47/multiagent_crosstranslate
Data Diversification: A Simple Strategy For Neural Machine Translation
https://github.com/nyu-dl/dl4mt-cdec
https://github.com/outcastofmusic/quick-nlp
Massive Exploration of Neural Machine Translation Architectures
https://github.com/owennju/fgrada
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation
https://github.com/pbcquoc/transformer
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/pcyin/pytorch_basic_nmt
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/penny9287/sockeye
https://github.com/philipperemy/keras-attention-mechanism
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/phueb/unmasked
https://github.com/poethan/MWE4MT
Incorporating Chinese Radicals Into Neural Machine Translation: Deeper Than Character Level
https://github.com/prabhakar267/LASER-improved
CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB
https://github.com/prachetaphadnis/NMT_Seq2Seq_LexicalModel
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/prithviraj-maurya/alexa-point-of-view-dataset
Converting the Point of View of Messages Spoken to Virtual Assistants
https://github.com/psunlpgroup/xsemplr
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/pwc-1/Paper-5/tree/main/mbart50
IndicBART: A Pre-trained Model for Indic Natural Language Generation
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