https://github.com/arianna-bis/freeorder-mt
공식 코드On the Difficulty of Translating Free-Order Case-Marking Languages
Papers with Code 아카이브
NMT
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Neural machine translation is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/artetxem/monoses
공식 코드https://github.com/artetxem/undreamt
공식 코드https://github.com/artetxem/vecmap
공식 코드https://github.com/awslabs/mlm-scoring
공식 코드https://github.com/awslabs/sockeye
공식 코드https://github.com/babangain/indicmmt
공식 코드Impact of Visual Context on Noisy Multimodal NMT: An Empirical Study for English to Indian Languages
https://github.com/babangain/unsup_questions_translation
공식 코드Reference Free Domain Adaptation for Translation of Noisy Questions with Question Specific Rewards
https://github.com/batman2013/e-commerce_test_sets
공식 코드Code-Switching for Enhancing NMT with Pre-Specified Translation
https://github.com/bcmi220/esc4nmt
공식 코드Explicit Sentence Compression for Neural Machine Translation
https://github.com/bert-nmt/bert-nmt
공식 코드https://github.com/bhaddow/pmindia-crawler
공식 코드PMIndia -- A Collection of Parallel Corpora of Languages of India
https://github.com/biaofuxmu/conslt
공식 코드A Token-level Contrastive Framework for Sign Language Translation
https://github.com/biblenlp/ebible-experiments
공식 코드The eBible Corpus: Data and Model Benchmarks for Bible Translation for Low-Resource Languages
https://github.com/boknilev/nmt-repr-analysis
공식 코드https://github.com/bonaventuredossou/ffr-v1
공식 코드https://github.com/bruceyg/dpo-mbr
공식 코드Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk Decoding
https://github.com/bytedance/neurst
공식 코드Towards Making the Most of BERT in Neural Machine Translation
https://github.com/bytedance/neurst/tree/master/examples/ctnmt
공식 코드Towards Making the Most of BERT in Neural Machine Translation
https://github.com/bzhangGo/zero
공식 코드Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation
https://github.com/cambridgeltl/ECNMT
공식 코드Emergent Communication Pretraining for Few-Shot Machine Translation
https://github.com/cbaziotis/nmt-pretraining-objectives
공식 코드Exploring Unsupervised Pretraining Objectives for Machine Translation
https://github.com/chaitanyamalaviya/lang-reps
공식 코드https://github.com/chaojun-wang/progressive-translation
공식 코드https://github.com/chiaramanna/gender-cue-integration-MT
공식 코드https://github.com/chrishokamp/constrained_decoding
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/cl-tohoku/PheMT
공식 코드PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents
https://github.com/clmbrs/communication-translation
공식 코드https://github.com/d-ataman/Char-NMT
공식 코드On the Importance of Word Boundaries in Character-level Neural Machine Translation
https://github.com/d-ataman/lmm
공식 코드A Latent Morphology Model for Open-Vocabulary Neural Machine Translation
https://github.com/dcsaunders/nmt-gender-rerank
공식 코드First the worst: Finding better gender translations during beam search
https://github.com/deep-spin/hallucinations-in-nmt
공식 코드https://github.com/deep-spin/ot-hallucination-detection
공식 코드Optimal Transport for Unsupervised Hallucination Detection in Neural Machine Translation
https://github.com/deep-spin/qaware-decode
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.