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/ZM7/A-PGNN
Personalized Graph Neural Networks with Attention Mechanism for Session-Aware Recommendation
https://github.com/ZM7/Personalizing-Graph-Neural-Networks-for-Session-basedRecommendation
Personalized Graph Neural Networks with Attention Mechanism for Session-Aware Recommendation
https://github.com/ZTianle/keras-tcn-solar
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/ZahinAkram/Unsupervised-Neural-Machine-Translation-Bangla-To-English-English-to-Bangla-
https://github.com/ZanzaKlaus/DeepTranslate
Massive Exploration of Neural Machine Translation Architectures
https://github.com/Zehui127/SQUAD_BERT
https://github.com/Zelgunn/CustomKerasLayers
Fixup Initialization: Residual Learning Without Normalization
https://github.com/ZhenYangIACAS/NMT
https://github.com/ZhouZhang233/SCU-MindSpore-UNnet3D-project
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
https://github.com/ZmeiGorynych/transformer_pytorch
https://github.com/a1085728420/seq2seq-mindspore
https://github.com/a414351664/Bert_MRC
https://github.com/aaaceo890/Attention
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/aaditkapoor/PDFExtract
HAT: Hardware-Aware Transformers for Efficient Natural Language Processing
https://github.com/aaronH45/sentimentAnalysis
https://github.com/aayushbansal/Recycle-GAN
https://github.com/abdelghanibelgaid/FNet-TensorFlow-PyTorch
https://github.com/abelriboulot/onnxt5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/abhaskumarsinha/Corpus2GPT
https://github.com/abhaskumarsinha/Keras-implementation-of-Transformer-Architecture
https://github.com/abhaskumarsinha/Keras-implementation-of-Transformer-Architecture/blob/main/Transformer.py
https://github.com/abhaskumarsinha/MinimalGPT
https://github.com/abhaskumarsinha/Seq2Seq-Bahdanau-Attention-based-Encoder-Decoder-Language-Translator
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/abhiagg92/bert_sentiment
https://github.com/abhilash1910/MiniClassifier
https://github.com/abhilashreddys/Fake-News-Article
https://github.com/abhishekr7/Summarization-of-Radiological-Reports
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/abhishekshakya/seq-2-seq-for-neural-machine-translation-english-to-hindi-
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/abmitra84/Machine_Translation
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/adelnabli/acid
https://github.com/adi2103/AML-CoVe
https://github.com/adityaagrawal7/speech-to-text-wavenet
https://github.com/advaithsrao/Chatbot_Seq2seq_model
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/af-ai-center/bert
https://github.com/af-ai-center/nerblackbox
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.