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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/vyraun/long-tailed
공식 코드https://github.com/wangke1996/translation-suggestion-psgd
공식 코드Easy Guided Decoding in Providing Suggestions for Interactive Machine Translation
https://github.com/wangqi1996/kok
공식 코드Non-Parametric Online Learning from Human Feedback for Neural Machine Translation
https://github.com/wangyong1122/dtn
공식 코드Go From the General to the Particular: Multi-Domain Translation with Domain Transformation Networks
https://github.com/wanyu2018umac/Self-Paced-Learning-for-Neural-Machine-Translation
공식 코드https://github.com/webspellchecker/unlp-2023-shared-task
공식 코드RedPenNet for Grammatical Error Correction: Outputs to Tokens, Attentions to Spans
https://github.com/wengsyx/low-resource-text-translation
공식 코드Towards Better Chinese-centric Neural Machine Translation for Low-resource Languages
https://github.com/wjko2/NMT-Adapt
공식 코드Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data
https://github.com/wjmacro/continualmt
공식 코드F-MALLOC: Feed-forward Memory Allocation for Continual Learning in Neural Machine Translation
https://github.com/wns823/NMT_SSP
공식 코드https://github.com/wszlong/sb-nmt
공식 코드https://github.com/wujunjie1998/twga
공식 코드Rethinking Targeted Adversarial Attacks For Neural Machine Translation
https://github.com/wwxu21/CER-MT
공식 코드https://github.com/wxjiao/Data-Rejuvenation
공식 코드Data Rejuvenation: Exploiting Inactive Training Examples for Neural Machine Translation
https://github.com/wxjiao/UncSamp
공식 코드Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation
https://github.com/xiaoyi0814/canmt
공식 코드https://github.com/xingniu/sockeye
공식 코드Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation
https://github.com/xllyu-nlp/dempt
공식 코드https://github.com/xuyuzhuang11/stylemt
공식 코드Pluggable Neural Machine Translation Models via Memory-augmented Adapters
https://github.com/yafuly/CoGnition
공식 코드On Compositional Generalization of Neural Machine Translation
https://github.com/yandex-research/msr
공식 코드https://github.com/yanyao-shen/fairseq
공식 코드https://github.com/ybisk/charNMT-noise
공식 코드Synthetic and Natural Noise Both Break Neural Machine Translation
https://github.com/yihongl1u/xlm_lang_dis
공식 코드On the Copying Problem of Unsupervised NMT: A Training Schedule with a Language Discriminator Loss
https://github.com/yilinyang7/fairseq_multi_fix
공식 코드Improving Multilingual Translation by Representation and Gradient Regularization
https://github.com/yimeng0701/CKD_pytorch
공식 코드Why Skip If You Can Combine: A Simple Knowledge Distillation Technique for Intermediate Layers
https://github.com/yulu-dada/learned-conf-nmt
공식 코드Learning Confidence for Transformer-based Neural Machine Translation
https://github.com/yunsukim86/sockeye-transfer
공식 코드Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies
https://github.com/yutongwang1216/reflectionllmmt
공식 코드TasTe: Teaching Large Language Models to Translate through Self-Reflection
https://github.com/yy-ye/mqm-analysis
공식 코드https://github.com/zanchangtong/ptvsri
공식 코드https://github.com/zdou0830/dynamic_select_weight
공식 코드Dynamic Data Selection and Weighting for Iterative Back-Translation
https://github.com/zhajiahe/Token_Drop
공식 코드https://github.com/zhengxxn/adaptive-knn-mt
공식 코드https://github.com/zhengxxn/uda-knn
공식 코드Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.