https://github.com/jind11/DAMT
공식 코드A Simple Baseline to Semi-Supervised Domain Adaptation for Machine Translation
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/jlibovicky/char-nmt
공식 코드Towards Reasonably-Sized Character-Level Transformer NMT by Finetuning Subword Systems
https://github.com/joeynmt/joeynmt
공식 코드https://github.com/judejiwoo/nmt
공식 코드https://github.com/karthikpuranik11/loresmt
공식 코드Attentive fine-tuning of Transformers for Translation of low-resourced languages @LoResMT 2021
https://github.com/kasunw22/sinhala-transliterator
공식 코드Sinhala Transliteration: A Comparative Analysis Between Rule-based and Seq2Seq Approaches
https://github.com/kcdichdangu/multilingualmt-uet-kc4.0
공식 코드https://github.com/kenrickkung/cantonesetranslation
공식 코드https://github.com/kritisingh24/active_learning_nmt
공식 코드https://github.com/lama-west/sparql_query_generation_aacl-ijcnl2022
공식 코드A Copy Mechanism for Handling Knowledge Base Elements in SPARQL Neural Machine Translation
https://github.com/lancopku/DeconvDec
공식 코드Deconvolution-Based Global Decoding for Neural Machine Translation
https://github.com/langtech-bsc/mt-evaluation
공식 코드MT-LENS: An all-in-one Toolkit for Better Machine Translation Evaluation
https://github.com/laniqo-public/do-not-change-me
공식 코드https://github.com/lavine-lmu/rmlnmt
공식 코드Improving Both Domain Robustness and Domain Adaptability in Machine Translation
https://github.com/lemon0830/codec
공식 코드https://github.com/lena-voita/good-translation-wrong-in-context
공식 코드https://github.com/lena-voita/the-story-of-heads
공식 코드Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation
https://github.com/lenacabrera/gb_mnmt
공식 코드https://github.com/leukas/charlevelmt
공식 코드Are Character-level Translations Worth the Wait? Comparing ByT5 and mT5 for Machine Translation
https://github.com/leukas/patchinggbst
공식 코드Patching Leaks in the Charformer for Efficient Character-Level Generation
https://github.com/levelower/vfa
공식 코드https://github.com/libeineu/Context-Aware
공식 코드Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation
https://github.com/libeineu/GPKD
공식 코드Learning Light-Weight Translation Models from Deep Transformer
https://github.com/libeineu/SDT-Training
공식 코드https://github.com/lighteternal/PENELOPIE
공식 코드PENELOPIE: Enabling Open Information Extraction for the Greek Language through Machine Translation
https://github.com/lin-tan/CURE
공식 코드CURE: Code-Aware Neural Machine Translation for Automatic Program Repair
https://github.com/lisasiyu/drtt
공식 코드https://github.com/lishaofeng/NLP_Backdoor
공식 코드https://github.com/litterbrother-xiao/overview-of-non-autoregressive-applications
공식 코드A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond
https://github.com/liyc7711/doc2doc
공식 코드P-Transformer: Towards Better Document-to-Document Neural Machine Translation
https://github.com/longyuewangdcu/tvsub
공식 코드Learning to Remember Translation History with a Continuous Cache
https://github.com/luosx18/biomedical-sentence-aligner
공식 코드Sentence Alignment with Parallel Documents Facilitates Biomedical Machine Translation
https://github.com/lvapeab/nmt-keras
공식 코드NMT-Keras: a Very Flexible Toolkit with a Focus on Interactive NMT and Online Learning
https://github.com/lyy1994/reformer
공식 코드https://github.com/mahfuzibnalam/terminology_evaluation
공식 코드On the Evaluation of Machine Translation for Terminology Consistency
https://github.com/manestay/ecxtra
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.