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Unsupervised Machine Translation

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평가표: 9
보존된 평가 기록: 40

Unsupervised machine translation is the task of doing machine translation without any translation resources at training time.

<span style="color:grey; opacity: 0.6">( Image credit: Phrase-Based & Neural Unsupervised Machine Translation )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/juletx/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/kaletap/nlp-experiments

A Probabilistic Formulation of Unsupervised Text Style Transfer

https://github.com/karpathy/build-nanogpt

Language Models are Few-Shot Learners

https://github.com/karpathy/llm.c

Language Models are Few-Shot Learners

https://github.com/keleog/PidginUNMT

Unsupervised Machine Translation Using Monolingual Corpora Only

https://github.com/kheeong/XLM_OWN

Phrase-Based & Neural Unsupervised Machine Translation

https://github.com/kyegomez/GPT3

Language Models are Few-Shot Learners

https://github.com/labdac/charlacompling

Word Translation Without Parallel Data

https://github.com/lambert-x/prolab

Language Models are Few-Shot Learners

https://github.com/longhao-chen/aicas2024

Language Models are Few-Shot Learners

https://github.com/ltruncel/Microsoft_Azure_50daysofudacity

Language Models are Few-Shot Learners

https://github.com/luofuli/DualLanST

Phrase-Based & Neural Unsupervised Machine Translation

https://github.com/luofuli/DualRL

Phrase-Based & Neural Unsupervised Machine Translation

https://github.com/makozi/AfrikaansNMT

Unsupervised Machine Translation Using Monolingual Corpora Only

https://github.com/maochf/MUSE

Word Translation Without Parallel Data

https://github.com/martiansideofthemoon/style-transfer-paraphrase

A Probabilistic Formulation of Unsupervised Text Style Transfer

https://github.com/mbzuai-paris/lm-evaluation-harness-atlas-chat

Language Models are Few-Shot Learners

https://github.com/michael-wzhu/mpnet_zh

MASS: Masked Sequence to Sequence Pre-training for Language Generation

https://github.com/microsoft/MPNet

MASS: Masked Sequence to Sequence Pre-training for Language Generation

https://github.com/migonch/unsupervised_mt

Unsupervised Machine Translation Using Monolingual Corpora Only

https://github.com/milmor/GPT

Language Models are Few-Shot Learners

https://github.com/mindspore-ai/models/tree/master/official/nlp/gpt

Language Models are Few-Shot Learners

https://github.com/mindspore-ai/models/tree/master/official/nlp/mass

MASS: Masked Sequence to Sequence Pre-training for Language Generation

https://github.com/national-center-for-ai-saudi-arabia/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/ncoop57/gpt-code-clippy

Language Models are Few-Shot Learners

https://github.com/neuralmagic/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/nlx-group/overlapy

Language Models are Few-Shot Learners

https://github.com/openbiolink/promptsource

Language Models are Few-Shot Learners

https://github.com/opengptx/lm-evaluation-harness

Language Models are Few-Shot Learners

https://github.com/postech-ami/smile-dataset

Language Models are Few-Shot Learners

https://github.com/pwc-1/Paper-9/tree/main/2/mbart

Multilingual Denoising Pre-training for Neural Machine Translation

https://github.com/ramanakshay/nanogpt

Language Models are Few-Shot Learners

https://github.com/roberttwomey/machine-imagination-isea

Language Models are Few-Shot Learners

https://github.com/roberttwomey/machine-imagination-workshop

Language Models are Few-Shot Learners

https://github.com/sabetAI/bucc-eval

Word Translation Without Parallel Data

https://github.com/sajidahmed12/CSE495-NLP-Assignment-Unsupervised-Neural-Machine-Translation

Word Translation Without Parallel Data

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