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/Helsinki-NLP/shared-info
https://github.com/HemaDevaSagar35/NeuralMachineTranslation-French2English
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/Henry-E/OpenNMT-py
https://github.com/HeshamElAbd/SelfAttentionLangModel
https://github.com/HzcIrving/DeepLearning_PlayGround
https://github.com/IBM/pytorch-seq2seq
https://github.com/ICSEG/M2TS
https://github.com/IDSIA/lmtool-fwms
https://github.com/IDSIA/lmtool-fwp
https://github.com/IDSIA/recurrent-fwp
https://github.com/IMvision12/keras-vision-models
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/IOB-Muenster/TEclass2
https://github.com/IS5882/Open-CyKG
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/Iammuratc/grainDetection
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
https://github.com/IlyaGusev/summarus
https://github.com/IpastorSan/seq2seq-with-attention-OCR-translation
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/IrishCoffee/cudnnMultiHeadAttention
https://github.com/IvanVassi/Seq2Seq-w-attention-dialogue-model
https://github.com/Iwontbecreative/Abstractive-summarization-OpenNMT
https://github.com/Izecson/sockeye-1.16.6
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/JNUpython/bert
https://github.com/JRC1995/Abstractive-Summarization
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/JRC1995/Machine-Translation-Transformers
https://github.com/JakubStefko/w2vf
https://github.com/JanAthmer/Compositional-generalization-capabillity-of-Transformer
https://github.com/JanakaSandaruwan/El-Kishky-Guzman-Document-Alignment
Massively Multilingual Document Alignment with Cross-lingual Sentence-Mover's Distance
https://github.com/Janice-M/tensorflow-lessons
https://github.com/Japanese-Image-Captioning/M2-transformer-for-Japanese
https://github.com/JayParks/transformer
https://github.com/JeheonPark596/sentimentsurvival
https://github.com/JiachenMao/TransfornerPrune
https://github.com/JohannLee1996/bert
https://github.com/JonasLeininger/ray-population-based-training
https://github.com/JonathanRaiman/tensorflow_qrnn
https://github.com/Joshuaoneheart/Fintech-Final-Report
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.