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/giovanniguidi/Seq-2-Seq-OCR
https://github.com/gitboyzorro5/tensor2tensor
https://github.com/glample/fastBPE
https://github.com/glouppe/info8010-deep-learning
https://github.com/gmum/dl-mo-2021
https://github.com/going-doer/paper2code
https://github.com/goldenbili/Bert_Test2
https://github.com/goldenbili/Bert_Test3
https://github.com/goldenbili/bert_lamb_pretrain
https://github.com/gongrennengzhi/nmt
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/gongshuangshuang/deep-text-corrector
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/goodluck4s/bert-
https://github.com/google-research/bigbird
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
https://github.com/google-research/language
Consistency by Agreement in Zero-shot Neural Machine Translation
https://github.com/google-research/t5x
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google-research/t5x_retrieval
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google-research/text-to-text-transfer-transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google/edward2
https://github.com/google/flax/tree/master/examples/wmt
https://github.com/google/seqio
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/gooofy/zbrain
https://github.com/gooppe/transformer-summarization
https://github.com/gordicaleksa/pytorch-original-transformer
https://github.com/gotutiyan/gec-metrics
https://github.com/grammatek/iceprondict
https://github.com/grantslatton/llama.cpp
https://github.com/graykode/gpt-2-Pytorch
https://github.com/graykode/nlp-tutorial
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/gretelai/gretel-synthetics
Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates
https://github.com/gtegner/hyper-gan
https://github.com/guillaume-chevalier/HAR-stacked-residual-bidir-LSTMs
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/guillaume-chevalier/Linear-Attention-Recurrent-Neural-Network
https://github.com/gulucaptain/dynamictrl
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/gulvarol/bsl1k
BSL-1K: Scaling up co-articulated sign language recognition using mouthing cues
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.