https://github.com/pwc-1/Paper-9/tree/main/2/mbart50
IndicBART: A Pre-trained Model for Indic Natural Language Generation
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/qiuguoxia/chatbotmodal
Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets
https://github.com/riversdie/chatbot
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/rxn4chemistry/OpenNMT-py
https://github.com/s-nlp/russe_detox_2022
https://github.com/safpla/AutoLossRelease
AutoLoss: Learning Discrete Schedules for Alternate Optimization
https://github.com/saisandeep97/Chat-botV2
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/salesforce/GeDi
https://github.com/sambit9238/deep_text_corrector
Addressing the Rare Word Problem in Neural Machine Translation
https://github.com/say2sarwar/DeepAcid
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/sblayush/Tensorflow-Attention
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/sblayush/summarization
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/sen-pai/audio-word2vec-pytorch
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/setu4993/convert-labse-tf-pt
https://github.com/shantipriyap/odia-nlp-resource-catalog
PMIndia -- A Collection of Parallel Corpora of Languages of India
https://github.com/shlokmehrotra/Convocare
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/simonjisu/NMT
https://github.com/siyuofzhou/CNNSeqToSeq
https://github.com/skoltech-nlp/russe_detox_2022
https://github.com/songchujun/joeynmt
https://github.com/soobinseo/transformer-tts
https://github.com/sooftware/attentions
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/soupstandstop/test
Massive Exploration of Neural Machine Translation Architectures
https://github.com/soyoung97/fairseq-mlm-scoring
https://github.com/soyoung97/mlm-scoring
https://github.com/sraj-whatdoesthedatasay/Capstone
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/stefan-it/deep-eos
Fully Character-Level Neural Machine Translation without Explicit Segmentation
https://github.com/sunnysinghnitb/text-corrector-software
Addressing the Rare Word Problem in Neural Machine Translation
https://github.com/sunnysinghnitb/text_corrector_software
Addressing the Rare Word Problem in Neural Machine Translation
https://github.com/superli3/CYRMPR
code2seq: Generating Sequences from Structured Representations of Code
https://github.com/superli3/codenavi
code2seq: Generating Sequences from Structured Representations of Code
https://github.com/tartunlp/transformertts
https://github.com/textflint/textflint
Synthetic and Natural Noise Both Break Neural Machine Translation
https://github.com/thinkwee/DPP_CNN_Summarization
https://github.com/thumt/THUMT
THUMT: An Open Source Toolkit for Neural Machine Translation
https://github.com/tilde-nlp/multilingual-nmt-data-prep
Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.