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Multimodal Machine Translation
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Multimodal machine translation is the task of doing machine translation with multiple data sources - for example, translating "a bird is flying over water" + an image of a bird over water to German text.
<span style="color:grey; opacity: 0.6">( Image credit: Findings of the Third Shared Task on Multimodal Machine Translation )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/idiotprofessorchen/bert.github.io
https://github.com/iejMac/ScriptWriter
https://github.com/ikergarcia1996/Simultaneous-Machine-Translation
https://github.com/imperialnlp/vtlm
Cross-lingual Visual Pre-training for Multimodal Machine Translation
https://github.com/insigh/THUMT
https://github.com/ishritam/Image-captioning-with-visual-attention
https://github.com/jadore801120/attention-is-all-you-need-pytorch
https://github.com/jageshmaharjan/BERT_Service
https://github.com/jaisenbe58r/NLP-Transformer_Translator
https://github.com/jamesdvance/predicting_clicks
https://github.com/jangjoongkeon/JK
https://github.com/jbdel/OMG_UMONS_submission
https://github.com/jean-kunz/ml_research_papers
https://github.com/jelifysh/Transformers
https://github.com/jeongchanyoung-1234/De-En-NMT
https://github.com/jerryyli/valhalla-nmt
https://github.com/jgcbrouns/Introducing-Sparsity-in-the-Transformer
https://github.com/jiahuei/Self-Critical-SCST-TensorFlow
https://github.com/jinzhenfan/BERT
https://github.com/jmnolte/hccnet
https://github.com/joebluems/Soul_Of_AI
https://github.com/joongbo/tta
https://github.com/joytianya/google_bert
https://github.com/junhahyung/bert_finetune
https://github.com/junhahyung/bert_transfer
https://github.com/kaituoxu/Speech-Transformer
https://github.com/kaituoxu/SpeechTransformer
https://github.com/karenacorn99/explore-bert
https://github.com/karpathy/makemore
https://github.com/karpathy/minGPT
https://github.com/karta282950/bert-multiclass
https://github.com/keiyamashita111/aaproject
https://github.com/kelly2016/multi-label-bert
https://github.com/keonlee9420/Parallel-Tacotron2
https://github.com/kh-kim/simple-nmt
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.