https://github.com/NVIDIA/OpenSeq2Seq
공식 코드Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq
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/Nalin21478/NLP-Project
공식 코드Sentiment Analysis Across Languages: Evaluation Before and After Machine Translation to English
https://github.com/Neutralzz/syntax_aware_local_attention
공식 코드https://github.com/Niger-Volta-LTI/yoruba-adr
공식 코드Attentive Sequence-to-Sequence Learning for Diacritic Restoration of Yorùbá Language Text
https://github.com/NiuTrans/LaMaTE
공식 코드Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation
https://github.com/NiuTrans/NiuTrans.NMT
공식 코드https://github.com/NurIren/Gender-Bias-in-TR-to-EN-MT-Models
공식 코드Examining Covert Gender Bias: A Case Study in Turkish and English Machine Translation Models
https://github.com/Oneplus/twpipe
공식 코드https://github.com/OpenBioLink/ITO
공식 코드A global analysis of metrics used for measuring performance in natural language processing
https://github.com/OpenNMT/OpenNMT
공식 코드https://github.com/PANXiao1994/mCOLT
공식 코드Contrastive Learning for Many-to-many Multilingual Neural Machine Translation
https://github.com/PANXiao1994/mRASP2
공식 코드Contrastive Learning for Many-to-many Multilingual Neural Machine Translation
https://github.com/PKU-TANGENT/SciDTB
공식 코드SciDTB: Discourse Dependency TreeBank for Scientific Abstracts
https://github.com/PaddlePaddle/Paddle
공식 코드https://github.com/PinjiaHe/StructureInvariantTesting
공식 코드https://github.com/PinzhenChen/sacreCOMET
공식 코드https://github.com/PortNLP/SHAP-MLLM-Analysis
공식 코드Beyond Data Quantity: Key Factors Driving Performance in Multilingual Language Models
https://github.com/PrekshaNema25/DiverstiyBasedAttentionMechanism
공식 코드Diversity driven Attention Model for Query-based Abstractive Summarization
https://github.com/Pzoom522/L1-Refinement
공식 코드Cross-Lingual Word Embedding Refinement by $\ell_{1}$ Norm Optimisation
https://github.com/QData/TextAttack
공식 코드TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP
https://github.com/QipengGuo/CycleGT
공식 코드CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training
https://github.com/RUCAIBox/TextBox
공식 코드TextBox 2.0: A Text Generation Library with Pre-trained Language Models
https://github.com/RayeRen/multilingual-kd-pytorch
공식 코드Multilingual Neural Machine Translation with Knowledge Distillation
https://github.com/RemiLeblond/SeaRNN-open
공식 코드https://github.com/ReneeYe/XSTNet
공식 코드End-to-end Speech Translation via Cross-modal Progressive Training
https://github.com/RishiHazra/Actively-reducing-redundancies-in-Active-Learning-for-Sequence-Tagging
공식 코드https://github.com/Roxot/AEVNMT
공식 코드https://github.com/Rudrabha/LipGAN
공식 코드https://github.com/SAP/software-documentation-data-set-for-machine-translation
공식 코드A Parallel Evaluation Data Set of Software Documentation with Document Structure Annotation
https://github.com/SCAlabUnical/CLEAR-Bias_LLM_benchmark
공식 코드https://github.com/SUTDNLP/ZGen
공식 코드https://github.com/Sachin19/seq2seq-con
공식 코드Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs
https://github.com/SaeedNajafi/ac-tagger
공식 코드https://github.com/Sagarika-Singh-99/indonesian_manhwa_translation
공식 코드Crossing Language Borders: A Pipeline for Indonesian Manhwa Translation
https://github.com/SageSELab/CodeSumStudy
공식 코드Code to Comment Translation: A Comparative Study on Model Effectiveness & Errors
https://github.com/SamuelGabriel/sqlbpe
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.