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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/sajidahmed12/CSE495-NLP-Assignment-Unsupervised-Neural-Machine-Translation
https://github.com/salesforce/GeDi
https://github.com/salesforce/pytorch-qrnn
https://github.com/sambanova/lm-evaluation-harness
https://github.com/sambit9238/deep_text_corrector
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/samnguyen8991/Facebook-MUSE
https://github.com/samsucik/t2t
https://github.com/samsunglabs/myqasr
FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech
https://github.com/samuellundberg/NeuralArchitectureSearch
https://github.com/samwisegamjeee/pytorch-transformers
https://github.com/samyakjain0112/Video-denoising_tensorflow-core-api-2.0
https://github.com/sanxing-chen/NMT2017-ZH-EN
Achieving Human Parity on Automatic Chinese to English News Translation
https://github.com/sarapapi/fbk-fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/saraswat/munkhdalai-nse
https://github.com/sarthaxxxxx/Attention-is-all-you-need
https://github.com/satyajitghana/TSAI-DeepNLP-END2.0
https://github.com/saurabhkulkarni77/BERT_multilabel
https://github.com/saurabhnlp/bert
https://github.com/sauram/Machine-Translation-using-Attention-Model
https://github.com/say2sarwar/DeepAcid
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/sayhitosandy/Chatbot
https://github.com/sberbank-ai/fusion_brain_aij2021
https://github.com/sblayush/Tensorflow-Attention
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/sblayush/summarization
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/schwartznir/AbstrEncap
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/scrayish/ML_NLP
https://github.com/seaweiqing/image2story
Neural Network Translation Models for Grammatical Error Correction
https://github.com/seaweiqing/neuraltalk_plus_charcnn
Neural Network Translation Models for Grammatical Error Correction
https://github.com/sebajoe/thresh
Thresh: A Unified, Customizable and Deployable Platform for Fine-Grained Text Evaluation
https://github.com/sebastianGehrmann/tensorflow-statereader
https://github.com/sebiancoder/bertscorevisualizer
https://github.com/seewoo5/KT
Creating A Neural Pedagogical Agent by Jointly Learning to Review and Assess
https://github.com/segment-any-text/wtpsplit
Where's the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation
https://github.com/selimseker/logogram-language-generator
Exploiting Similarities among Languages for Machine Translation
https://github.com/selmiss/gp-tlstgcn
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.