https://github.com/GXimingLu/a_star_neurologic
공식 코드NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics
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/GalaxyGHz/Diploma
공식 코드https://github.com/Glaceon31/PR4NMT
공식 코드Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization
https://github.com/Glaciohound/Chimera-SLT
공식 코드Learning Shared Semantic Space for Speech-to-Text Translation
https://github.com/Glaciohound/Chimera-ST
공식 코드Learning Shared Semantic Space for Speech-to-Text Translation
https://github.com/GreenParachute/mqm-eng-cro
공식 코드https://github.com/HC-Guo/Owl
공식 코드https://github.com/HC-Guo/RedTrans
공식 코드Redefining Machine Translation on Social Network Services with Large Language Models
https://github.com/Heidelberg-NLP/amr-metric-suite
공식 코드https://github.com/Heidelberg-NLP/xsrl_mbert_aligner
공식 코드X-SRL: A Parallel Cross-Lingual Semantic Role Labeling Dataset
https://github.com/Helsinki-NLP/OPUS-MT-train
공식 코드https://github.com/Helsinki-NLP/Tatoeba-Challenge
공식 코드The Tatoeba Translation Challenge -- Realistic Data Sets for Low Resource and Multilingual MT
https://github.com/IBM/Graph2Tree
공식 코드https://github.com/IBM/transition-amr-parser
공식 코드https://github.com/IITB-OpenOCRCorrect/iitb-openocr-digit-tool
공식 코드UDAAN: Machine Learning based Post-Editing tool for Document Translation
https://github.com/Imagist-Shuo/UNMT-SPR
공식 코드Unsupervised Neural Machine Translation with SMT as Posterior Regularization
https://github.com/ImperialNLP/BertGen
공식 코드https://github.com/ImperialNLP/MMT-Delib
공식 코드https://github.com/ImperialNLP/pysimt
공식 코드https://github.com/Izecson/fairseq-editor
공식 코드https://github.com/Izecson/saml-nmt
공식 코드Differentiable Sampling with Flexible Reference Word Order for Neural Machine Translation
https://github.com/JetBrains-Research/codegen-metrics
공식 코드Out of the BLEU: how should we assess quality of the Code Generation models?
https://github.com/Jingjing-NLP/VOLT
공식 코드Vocabulary Learning via Optimal Transport for Neural Machine Translation
https://github.com/JoyeBright/ICLviaQE
공식 코드Guiding In-Context Learning of LLMs through Quality Estimation for Machine Translation
https://github.com/JunW15/Monolingual-Attack
공식 코드https://github.com/JunW15/NumberTest
공식 코드As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation
https://github.com/Kabir-Ehsan/Transformer_on_FPGA
공식 코드A Runtime-Adaptive Transformer Neural Network Accelerator on FPGAs
https://github.com/Kaleidophon/deep-significance
공식 코드https://github.com/Katsumata420/HITS_Ranking
공식 코드Graph-based Filtering of Out-of-Vocabulary Words for Encoder-Decoder Models
https://github.com/KentonMurray/ProxGradPytorch
공식 코드https://github.com/KhrulkovV/tt-pytorch
공식 코드https://github.com/KurdishBLARK/HawramiClassification
공식 코드https://github.com/KurdishBLARK/InterdialectCorpus
공식 코드Leveraging Multilingual News Websites for Building a Kurdish Parallel Corpus
https://github.com/Kyubyong/quasi-rnn
공식 코드Machine Translation of Low-Resource Spoken Dialects: Strategies for Normalizing Swiss German
https://github.com/LUMIA-Group/Leveraging-Grammar-Induction
공식 코드Leveraging Grammar Induction for Language Understanding and Generation
https://github.com/LZhengisme/CODA
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.