https://github.com/CRIPAC-DIG/A-PGNN
공식 코드Personalized Graph Neural Networks with Attention Mechanism for Session-Aware Recommendation
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/CZWin32768/xnlg
공식 코드https://github.com/Cartus/DCGCN
공식 코드Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning
https://github.com/ChenRocks/Distill-BERT-Textgen
공식 코드https://github.com/ChengyueGongR/FrequencyAgnostic
공식 코드https://github.com/ChengyueGongR/advsoft
공식 코드https://github.com/CodeMixedUniversalDependencies/UD_Hindi_English
공식 코드Universal Dependency Parsing for Hindi-English Code-switching
https://github.com/Cosmos-Break/transfer-mt-submit
공식 코드https://github.com/CyberAgentAILab/adaptive-mbr
공식 코드Hyperparameter-Free Approach for Faster Minimum Bayes Risk Decoding
https://github.com/D-X-Y/Awesome-AutoDL
공식 코드Automated Deep Learning: Neural Architecture Search Is Not the End
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/DannielSilva/RS-M-CLIP
공식 코드Multilingual Vision-Language Pre-training for the Remote Sensing Domain
https://github.com/DeepLearnXMU/ABD-NMT
공식 코드Asynchronous Bidirectional Decoding for Neural Machine Translation
https://github.com/DeepLearnXMU/MM-DCCN
공식 코드Dynamic Context-guided Capsule Network for Multimodal 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/DeepLearnXMU/VNMT
공식 코드https://github.com/DevSinghSachan/multilingual_nmt
공식 코드Parameter Sharing Methods for Multilingual Self-Attentional Translation Models
https://github.com/Disiok/poetry-seq2seq
공식 코드Hierarchical Attention: What Really Counts in Various NLP Tasks
https://github.com/Dodiom/dodiom
공식 코드https://github.com/ELITR/wmt19-elitr-testsuite
공식 코드https://github.com/ESanchezLozano/GANnotation
공식 코드Triple consistency loss for pairing distributions in GAN-based face synthesis
https://github.com/EdinburghNLP/nematus
공식 코드https://github.com/EdinburghNLP/opus-100-corpus
공식 코드Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation
https://github.com/Edward-Sun/structured-nart
공식 코드https://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/ElliottYan/Multi_Unit_Transformer
공식 코드https://github.com/EncryptedBinary/BanglaDialecto
공식 코드BanglaDialecto: An End-to-End AI-Powered Regional Speech Standardization
https://github.com/EriCongMa/MTKD_TIMT
공식 코드Multi-Teacher Knowledge Distillation For Text Image Machine Translation
https://github.com/Eric-Wallace/adversarial-mt
공식 코드Imitation Attacks and Defenses for Black-box Machine Translation Systems
https://github.com/EricGuo5513/TM2T
공식 코드TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts
https://github.com/EurekaForNLP/SM2
공식 코드Self-Modifying State Modeling for Simultaneous Machine Translation
https://github.com/FrancisGregoire/parSentExtract
공식 코드https://github.com/GGLAB-KU/gecturk
공식 코드GECTurk: Grammatical Error Correction and Detection Dataset for Turkish
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.