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/laniqo-public/grammar-data-mtsummit25
https://github.com/laraolmos/madrid-nlp-meetup
SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation
https://github.com/laserene/English-German-Translation-System
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/ldulcic/chatbot
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/leaderj1001/Bag-of-MLP
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/leduckhai/Global-Wheat-Detection-Kaggle
MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech Translation
https://github.com/leehyunjun-seoultech/seoultech
https://github.com/leejieun51/transformer
https://github.com/lehinevych/SAAN
https://github.com/lehoanganh298/BERT-Question-Answering
https://github.com/lennonzurich/lalala
https://github.com/leob03/Image_captionning
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/leohsuofnthu/Pytorch-IterativeFCN
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/mlp_family
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/lesterpjy/numeric-t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/libertfan/man
Mask Attention Networks: Rethinking and Strengthen Transformer
https://github.com/lidongliang666/cv_deep_learning
https://github.com/liernisestorain/zero-shot-dual-MT
https://github.com/lif31up/attention-is-all-you-need
https://github.com/lif31up/extended-BERT-for-low-rank-adaption
https://github.com/lifesailor/data-driven-predictive-maintenance
Deep Learning and Its Applications to Machine Health Monitoring: A Survey
https://github.com/liguigui/speech-to-text-wavenet
https://github.com/linxi159/TCN
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/liqichen6688/duo-attention
https://github.com/littell/NamedTransformer
https://github.com/littleflow3r/Japanese_English_Machine_Translation_Seq2Seq
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/littleflow3r/SeqtoSeq_learning_for_machine_translation
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/littleflow3r/Sequence_to_sequence_learning_for_machine_translation
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/littleflow3r/Sequence_to_sequence_learning_with_NN
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/liuruiyang98/Jittor-MLP
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/liyaSileshi/amharic-sentiment-analysis
https://github.com/liyongze/lstm_speaker_verification
Attention-Based Models for Text-Dependent Speaker Verification
https://github.com/lizzylizzylizzy/evolvingattention
https://github.com/lkfo415579/MT-Readling-List
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/lkfo415579/amun
Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.