https://github.com/SamuelCahyawijaya/indobenchmark-toolkit
IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation
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/Samyu0304/thought-propagation
https://github.com/Satan012/BERT
https://github.com/ScientiaEtVeritas/NeuralMachineTranslation
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/SdahlSean/RangerOptimizerTensorflow
https://github.com/SeonbeomKim/Python-Bype_Pair_Encoding
https://github.com/SeonbeomKim/Python-Byte_Pair_Encoding
https://github.com/SeoroMin/transformer_pytorch
https://github.com/SeoroMin/transformer_pytorch_ver2
https://github.com/SergiBragos/DLAI-2021-project
Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network
https://github.com/Sharif-SLPL/t5-fa
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/ShashwatNigam99/MARBLER
Emergence of Grounded Compositional Language in Multi-Agent Populations
https://github.com/Shasvat-Desai/Shasvat-Desai
https://github.com/ShiYaya/Awesome_Evaluation_Metrics_for_Text_Generation
https://github.com/Shinya-Kouda/kgc
https://github.com/ShivamRajSharma/Transformer-Architectures-From-Scratch
https://github.com/Shivendra-psc/speechbot
https://github.com/ShotDownDiane/tcn-master
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/Shrajan/AAAI-2022
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
https://github.com/Shrajan/IB_U_Nets
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
https://github.com/Shubham-SK/TreeOverAte
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/Shubham-SK/kronos
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/ShuvenduBikash/transformer_spelling_corrector
https://github.com/ShwetaBaranwal/Transformer
https://github.com/SimengSun/CIS530-project
SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation
https://github.com/SimonDele/Glossary
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/Skumarr53/Attention-is-All-you-Need-PyTorch
https://github.com/Smerity/keras_snli
https://github.com/Somefive/XNLI
https://github.com/Songweiping/TCN-TF
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/SpikeKing/My-Bert
https://github.com/StatNLP/ada4asr
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/StatNLP/joeynmt
https://github.com/SteffenEger/ocr_spelling_deuparl
A Large-Scale Comparison of Historical Text Normalization Systems
https://github.com/StevenLai1994/AM_and_LM
https://github.com/StillKeepTry/Transformer-PyTorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.