https://github.com/chanzy3/11747_Final_Project
공식 코드Zero-Shot Language Transfer vs Iterative Back Translation for Unsupervised Machine Translation
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/chaojun-wang/progressive-translation
공식 코드https://github.com/chenllliang/MLS
공식 코드Focus on the Target's Vocabulary: Masked Label Smoothing for Machine Translation
https://github.com/chenllliang/off-target-mnmt
공식 코드On the Off-Target Problem of Zero-Shot Multilingual Neural Machine Translation
https://github.com/chenlong164/peit
공식 코드Property Enhanced Instruction Tuning for Multi-task Molecule Generation with Large Language Models
https://github.com/chenyangh/OTTAWA
공식 코드https://github.com/chenyangh/dslp
공식 코드Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision
https://github.com/chenziku/NMT-MCTS
공식 코드https://github.com/chiaramanna/gender-cue-integration-MT
공식 코드https://github.com/chqiwang/sa-nmt
공식 코드https://github.com/chrisemezue/IgboASR
공식 코드https://github.com/chrishokamp/constrained_decoding
공식 코드Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search
https://github.com/chrishokamp/dynamic-transformer-ensembles
공식 코드DynE: Dynamic Ensemble Decoding for Multi-Document Summarization
https://github.com/chrisisking/caribbean-creole-languages-translation
공식 코드https://github.com/cindyxinyiwang/DataSelection
공식 코드https://github.com/cindyxinyiwang/SDE
공식 코드Multilingual Neural Machine Translation With Soft Decoupled Encoding
https://github.com/cindyxinyiwang/TrDec_pytorch
공식 코드https://github.com/cindyxinyiwang/deep-latent-sequence-model
공식 코드A Probabilistic Formulation of Unsupervised Text Style Transfer
https://github.com/cindyxinyiwang/multiDDS
공식 코드Balancing Training for Multilingual Neural Machine Translation
https://github.com/cisnlp/graph-align
공식 코드https://github.com/cisnlp/manchu-in-context-mt
공식 코드Understanding In-Context Machine Translation for Low-Resource Languages: A Case Study on Manchu
https://github.com/cisnlp/translico
공식 코드https://github.com/cisnlp/transliteration-ppa
공식 코드https://github.com/cisnlp/transmi
공식 코드https://github.com/ckbjimmy/p2c
공식 코드https://github.com/cl-tohoku/PheMT
공식 코드PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents
https://github.com/cliang1453/sage
공식 코드https://github.com/cluebbers/dpo-rlhf-paraphrase-types
공식 코드Enhancing Paraphrase Type Generation: The Impact of DPO and RLHF Evaluated with Human-Ranked Data
https://github.com/cmhcbb/Seq2Sick
공식 코드Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples
https://github.com/cmu-llab/gpt_mt_benchmark
공식 코드ChatGPT MT: Competitive for High- (but not Low-) Resource Languages
https://github.com/coastalcph/histnorm
공식 코드A Large-Scale Comparison of Historical Text Normalization Systems
https://github.com/codecaution/evomoe
공식 코드EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate
https://github.com/codogogo/xling-eval
공식 코드https://github.com/cognitiveailab/darmok
공식 코드https://github.com/coldmist-lu/erroranalysis_prompt
공식 코드Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Models
https://github.com/coldmist-lu/mqm_ape
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.