https://github.com/yuriak/PPDAI
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
Papers with Code 아카이브
Machine Translation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/yydai/bert_test
https://github.com/zankner/Transformer
https://github.com/zapplea/bert
https://github.com/zbp-xxxp/R-Drop-Paddle
https://github.com/zepx/pytorch-weight-prune
Compression of Neural Machine Translation Models via Pruning
https://github.com/zer0sh0t/artificial_intelligence/tree/master/transformer
https://github.com/zhang-huihui/git-repository
https://github.com/zhang0jhon/AttentionOCR
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/zhangbo2008/best_pytorch_transformer
https://github.com/zhangtj1996/lookahead-sgd-adam-rmsprop-
https://github.com/zhangyuwangumass/Glyph-based-Chinese-Character-Embedding
https://github.com/zhaopku/LSTM-Jump-for-text-skimming
https://github.com/zhdbwe/Paper-DailyReading
https://github.com/zhijing-jin/GEC_papers
Improving Grammatical Error Correction with Machine Translation Pairs
https://github.com/zhiqic/chartreader
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/zhong110020/TensorFlow_TCN
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/zhong110020/Tensorflow-TCN
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/zhong110020/keras-tcn
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/zhong110020/pytorch_TCN
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/zhou059/w266-project
https://github.com/zipeiyang/DeepSpeechRecognition
https://github.com/ziyi-yang/lir
LAReQA: Language-agnostic answer retrieval from a multilingual pool
https://github.com/zll1996/TCN
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/zomux/neuralcompressor
Compressing Word Embeddings via Deep Compositional Code Learning
https://github.com/zpc-666/Paddle-R-Drop
https://github.com/zphang/lm_evaluation_harness
https://github.com/zsweet/BERT_zsw
https://github.com/zurichnlp/mbr
https://github.com/zxy14120448/Summary
https://github.com/zyddnys/manga-image-translator
https://github.com/zysite/post
https://github.com/zzzxiaohong/icbt
https://gitlab.com/birder/birder
ResMLP: Feedforward networks for image classification with data-efficient training
https://huggingface.co/google/roberta2roberta_L-24_wikisplit
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.