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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/virsagothethird/Korean-English-translator
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/vishnuanil122/Bert-Binary-Text-Classifier
https://github.com/vivekgohel56/Neural-machine-translation-english-to-polish
https://github.com/vivekpandian08/French_English_Translation-NMT-
https://github.com/voiler/populationbasedtraining
https://github.com/volcengine/vegiantmodel
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/vpj/jax_transformer
https://github.com/vsatyav007/repo-eventdetection
https://github.com/vulus98/rethinking-attention
https://github.com/vvvm23/TchAIkovsky-Legacy
https://github.com/vyraun/Half-Size
On Dimensional Linguistic Properties of the Word Embedding Space
https://github.com/walkacross/transformer-pytorch
https://github.com/wang-h/neuralcompressor
Compressing Word Embeddings via Deep Compositional Code Learning
https://github.com/wangclnlp/deepspeed-chat-extension
ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation
https://github.com/wangcongcong123/ttt
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/wangmz15/Chinese-Error-Correction-with-THUMT
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/wanyu2018umac/Unsupervised_Dialect_Translation
Unsupervised Neural Dialect Translation with Commonality and Diversity Modeling
https://github.com/wayalhruhi/gogle_bert
https://github.com/wchh127/yykf
https://github.com/weipingfu/qgeval
https://github.com/weiwang2330/sparse-structured-attention
A Regularized Framework for Sparse and Structured Neural Attention
https://github.com/weiylu/NLP
https://github.com/whr94621/NJUNMT-pytorch
https://github.com/why2000/DuReader-bert
https://github.com/windsuzu/Enhance-Chinese-to-Japanese-Neural-Machine-Translation
https://github.com/windsuzu/improving-NMT-ch-jp
https://github.com/windweller/PragmaticVQA
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/wish44165/A-New-Perspective-for-Shuttlecock-Hitting-Event-Detection
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/wkcn/LookaheadOptimizer-mx
https://github.com/wl-research/nubia
NUBIA: NeUral Based Interchangeability Assessor for Text Generation
https://github.com/wm-semeru/hephaestus
https://github.com/wolny/pytorch-3dunet
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
https://github.com/woozzu/dong_iccv_2017
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
https://github.com/wxt1997/Transformer-Transducer
https://github.com/wyu97/Easy-use-BART
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.