https://github.com/deeplearnxmu/bigvideo-vmt
공식 코드BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation
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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/deeplearnxmu/knn-mt-dr
공식 코드Efficient k-Nearest-Neighbor Machine Translation with Dynamic Retrieval
https://github.com/deeplearnxmu/revisedkey-knn-mt
공식 코드https://github.com/deeplearnxmu/robust-knn-mt
공식 코드https://github.com/demelin/detecting_wsd_biases_for_nmt
공식 코드https://github.com/dessertlab/Shellcode_IA32
공식 코드Shellcode_IA32: A Dataset for Automatic Shellcode Generation
https://github.com/devaansh100/cliptrans
공식 코드CLIPTrans: Transferring Visual Knowledge with Pre-trained Models for Multimodal Machine Translation
https://github.com/dimitarsh1/nmt-ecofootprint
공식 코드The Ecological Footprint of Neural Machine Translation Systems
https://github.com/dipteshkanojia/challengeCognateFF
공식 코드Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages
https://github.com/dirkiedai/sk-mt
공식 코드https://github.com/dmis-lab/AdvSR
공식 코드Adversarial Subword Regularization for Robust Neural Machine Translation
https://github.com/dsfsi/gov-za-multilingual
공식 코드Preparing the Vuk'uzenzele and ZA-gov-multilingual South African multilingual corpora
https://github.com/dsfsi/vukuzenzele-nlp
공식 코드Preparing the Vuk'uzenzele and ZA-gov-multilingual South African multilingual corpora
https://github.com/ducthanhtran/sockeye_document_context
공식 코드When and Why is Document-level Context Useful in Neural Machine Translation?
https://github.com/duguyue100/wmt-en2wubi
공식 코드Character-level Chinese-English Translation through ASCII Encoding
https://github.com/e-bug/nmt-difficulty
공식 코드https://github.com/eaplatanios/symphony-mt
공식 코드Contextual Parameter Generation for Universal Neural Machine Translation
https://github.com/elbayadm/OnlineMT-Evaluation
공식 코드Online Versus Offline NMT Quality: An In-depth Analysis on English-German and German-English
https://github.com/emjotde/mosesdecoder_nmt
공식 코드https://github.com/emnlpanon/mt-paper
공식 코드Multi-Domain Adaptation in Neural Machine Translation Through Multidimensional Tagging
https://github.com/ewdowiak/Sicilian_Translator
공식 코드https://github.com/exqrch/indonesiannmt
공식 코드https://github.com/facebookresearch/LASER
공식 코드CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB
https://github.com/facebookresearch/UnsupervisedMT
공식 코드https://github.com/facebookresearch/UnsupervisedQA
공식 코드https://github.com/fadedcosine/knn-kd
공식 코드Nearest Neighbor Knowledge Distillation for Neural Machine Translation
https://github.com/fandongmeng/DTMT_InDec
공식 코드DTMT: A Novel Deep Transition Architecture for Neural Machine Translation
https://github.com/fdalvi/NeuroX
공식 코드What Is One Grain of Sand in the Desert? Analyzing Individual Neurons in Deep NLP Models
https://github.com/fe1ixxu/BiBERT
공식 코드BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation
https://github.com/freesunshine0316/semantic-nmt
공식 코드https://github.com/fstahlberg/ucam-scripts
공식 코드An Operation Sequence Model for Explainable Neural Machine Translation
https://github.com/ghchen18/acl22-sixtp
공식 코드Towards Making the Most of Multilingual Pretraining for Zero-Shot Neural Machine Translation
https://github.com/ghchen18/emnlp2021-sixt
공식 코드Zero-shot Cross-lingual Transfer of Neural Machine Translation with Multilingual Pretrained Encoders
https://github.com/google-research/google-research
공식 코드https://github.com/google-research/language/tree/master/language/labs/consistent_zero_shot_nmt
공식 코드Consistency by Agreement in Zero-shot Neural Machine Translation
https://github.com/google-research/url-nlp
공식 코드Bilex Rx: Lexical Data Augmentation for Massively Multilingual Machine Translation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.