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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/Kelechukwu1/PidginUNMT
https://github.com/KevinYoung98/Seq2Seq-PyTorch
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/KuldeepSangwan/MachineTranslation
Neural Machine Translation for Low-Resourced Indian Languages
https://github.com/Liut2016/stackedResBiLSTM
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/LiyuanLucasLiu/Transforemr-Clinic
https://github.com/LiyuanLucasLiu/Transformer-Clinic
https://github.com/ManzoorElahi/organic-chemistry-reaction-prediction-using-NMT
Asynchronous Bidirectional Decoding for Neural Machine Translation
https://github.com/Microsoft/NPMT
https://github.com/MindSpore-paper-code-3/code9/tree/main/gnmt_v2
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/MohammadJavadD/APD_EEG
Amplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer Learning
https://github.com/Munna-Manoj/Team6_FastSpeech2_TTS
https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Translation/GNMT
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/Nick-Zhao-Engr/Machine-Translation
Document-Level Neural Machine Translation with Hierarchical Attention Networks
https://github.com/Nirvanabc/seq2seq-rnn
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/NonameAuPlatal/Dual_Learning
https://github.com/OpenNMT/OpenNMT-py
https://github.com/OpenNMT/OpenNMT-tf
Guided Alignment Training for Topic-Aware Neural Machine Translation
https://github.com/PaddlePaddle/PaddleSpeech
https://github.com/PaulSudarshan/Language-Classification-Using-Naive-Bayes-Algorithm
https://github.com/Pendulibrium/ai-visual-storytelling-seq2seq
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/RobustNLP/TestTranslation
https://github.com/SFFAI-AIKT/AIKT-Natural_Language_Processing
A Survey of Deep Learning Techniques for Neural Machine Translation
https://github.com/ScientiaEtVeritas/NeuralMachineTranslation
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/SeonbeomKim/Python-Bype_Pair_Encoding
https://github.com/SeonbeomKim/Python-Byte_Pair_Encoding
https://github.com/Shubham-SK/TreeOverAte
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/Shubham-SK/kronos
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/StatNLP/joeynmt
https://github.com/StuartCHAN/KARL
https://github.com/T9-LIN/MSc-Project
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/THUNLP-MT/THUMT
THUMT: An Open Source Toolkit for Neural Machine Translation
https://github.com/ThAIKeras/bert
https://github.com/Unbabel/OpenNMT-py
https://github.com/Unbabel/sparse_constrained_attention
Sparse and Constrained Attention for Neural Machine Translation
https://github.com/VKCOM/YouTokenToMe
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.