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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/tanyuqian/texar-pytorch
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
https://github.com/tartunlp/transformertts
https://github.com/tatp22/linformer-pytorch
https://github.com/tatp22/multidim-positional-encoding
https://github.com/tbmoon/LANL_Earthquake_Prediction
https://github.com/tcnguyen/bert
https://github.com/tcxdgit/MachineTranslation_t2t
https://github.com/tdiggelm/nn-experiments
Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction
https://github.com/tensorflow/models/tree/master/research/neural_gpu
https://github.com/tensorflow/models/tree/master/research/skip_thoughts
Exploiting Similarities among Languages for Machine Translation
https://github.com/tensorops/TransformerX
https://github.com/terne/dtuproject
Deep Reinforcement Learning for Programming Language Correction
https://github.com/text-machine-lab/transformerpy
https://github.com/textflint/textflint
Synthetic and Natural Noise Both Break Neural Machine Translation
https://github.com/thanhlong1997/bert_quora
https://github.com/the-Quert/iNLPfun
https://github.com/theQuert/inlpfun
https://github.com/theamrzaki/text_summurization_abstractive_methods
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/thecodemasterk/BERT
https://github.com/thecodemasterk/Text-to-Text-transfer-transformers
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/thefonseca/factorsum
https://github.com/thespectrewithin/joint_align
Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework
https://github.com/thomlake/pytorch-attention
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/thompsonb/fairseq-smrt
Simulated Multiple Reference Training Improves Low-Resource Machine Translation
https://github.com/thompsonb/mt-metrics-eval
Ties Matter: Meta-Evaluating Modern Metrics with Pairwise Accuracy and Tie Calibration
https://github.com/thompsonb/prism_bitext_filter
Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings
https://github.com/thu-coai/NAST
A Probabilistic Formulation of Unsupervised Text Style Transfer
https://github.com/thu-keg/omnievent
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/thudm/swissarmytransformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/thumt/THUMT
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/thunlp/TensorFlow-Summarization
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/thunlp/readin
Sub-Character Tokenization for Chinese Pretrained Language Models
https://github.com/tianhai123/-
https://github.com/tilde-nlp/multilingual-nmt-data-prep
Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
https://github.com/tilde-nlp/nematus
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.