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/ays-dev/keras-transformer
https://github.com/b-etienne/Seq2seq-PyTorch
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/babangain/translation
https://github.com/babylonhealth/MultilingualFactorAnalysis
https://github.com/babylonhealth/fastText_multilingual
Exploiting Similarities among Languages for Machine Translation
https://github.com/bagequan/tencent-transformer-with-disagreement
Agreement-based Joint Training for Bidirectional Attention-based Neural Machine Translation
https://github.com/baidu-research/HNN
https://github.com/bakirillov/rgramlib
R-grams: Unsupervised Learning of Semantic Units in Natural Language
https://github.com/bam098/deep_knn
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning
https://github.com/bangoc123/transformer
https://github.com/baotramduong/Sentiment-Analysis-with-Deep-Learning-using-BERT
https://github.com/baotramduong/Twitter-Sentiment-Analysis-with-Deep-Learning-using-BERT
https://github.com/barnerwothers/MUSE
https://github.com/bartzi/chainer-transformer
https://github.com/basile-bron/Artificial-Intelligence-Lexical
https://github.com/bayer-science-for-a-better-life/data2text-bioleaflets
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/beinborn/SemanticDrift
https://github.com/ben-davidson-6/fixup
Fixup Initialization: Residual Learning Without Normalization
https://github.com/bengittelson/sow-reap-paraphrasing
Neural Syntactic Preordering for Controlled Paraphrase Generation
https://github.com/benjaminbeilharz/hierarchical-reinforcement-learning
https://github.com/benjamintaiwo/Attention
https://github.com/bentrevett/pytorch-seq2seq
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/benywon/ComQA
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
https://github.com/bfopengradient/NLP_text_summarization_apps
https://github.com/bhaddow/dev-nematus
https://github.com/bhavitvyamalik/bert
https://github.com/big-o/transvec
Exploiting Similarities among Languages for Machine Translation
https://github.com/bigai-nlco/cream
AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation
https://github.com/bigcode-project/starcoder
https://github.com/bigscience-workshop/Megatron-DeepSpeed
https://github.com/bigscience-workshop/architecture-objective
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/bigscience-workshop/promptsource
https://github.com/binhetech/bert-application
https://github.com/biyoml/End-to-End-Mandarin-ASR
https://github.com/biyoml/Pytorch-End-to-End-ASR-on-TIMIT
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.