Papers with Code 아카이브
NMT
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 0
- 보존된 평가 기록
- 0
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/marian-nmt/sotastream
공식 코드SOTASTREAM: A Streaming Approach to Machine Translation Training
https://github.com/marliesvanderwees/dds-nmt
공식 코드https://github.com/marziehf/IdiomTranslationDS
공식 코드Examining the Tip of the Iceberg: A Data Set for Idiom Translation
https://github.com/marziehf/variation-generation
공식 코드The Unreasonable Volatility of Neural Machine Translation Models
https://github.com/masakhane-io/afridoc-mt
공식 코드https://github.com/masoudjs/simalign
공식 코드https://github.com/mattiadg/SR-NMT
공식 코드https://github.com/maxwell1447/fairseq
공식 코드Towards Example-Based NMT with Multi-Levenshtein Transformers
https://github.com/may-/joeys2t
공식 코드https://github.com/melkor169/LeadSheetGen_Valence
공식 코드Generating Lead Sheets with Affect: A Novel Conditional seq2seq Framework
https://github.com/microsoft/automoe
공식 코드https://github.com/middlekisser/GMNMT
공식 코드A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation
https://github.com/mikahama/murre
공식 코드Automatic Dialect Adaptation in Finnish and its Effect on Perceived Creativity
https://github.com/mikahama/natas
공식 코드https://github.com/mitramir55/prose2poem
공식 코드Prose2Poem: The Blessing of Transformers in Translating Prose to Persian Poetry
https://github.com/modit-team/MODIT
공식 코드https://github.com/moore3930/calibrating-llm-mt
공식 코드Calibrating Translation Decoding with Quality Estimation on LLMs
https://github.com/mt-upc/resetox
공식 코드ReSeTOX: Re-learning attention weights for toxicity mitigation in machine translation
https://github.com/mt-upc/transformer-contributions-nmt
공식 코드https://github.com/naist-nlp/knn-seq
공식 코드https://github.com/namisan/exdeep-nmt
공식 코드https://github.com/naver-ai/MetricMT
공식 코드Reward Optimization for Neural Machine Translation with Learned Metrics
https://github.com/neulab/word-embeddings-for-nmt
공식 코드When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?
https://github.com/neulab/xnmt
공식 코드https://github.com/nicolae-teodor-pavel/commit-message-generation
공식 코드A Sketch-Based Neural Model for Generating Commit Messages from Diffs
https://github.com/niyatibafna/pgns-for-lrmt
공식 코드Pointer-Generator Networks for Low-Resource Machine Translation: Don't Copy That!
https://github.com/nlp2ct/consisttl
공식 코드ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation
https://github.com/nlpcuom/quality-matters
공식 코드Quality Does Matter: A Detailed Look at the Quality and Utility of Web-Mined Parallel Corpora
https://github.com/nttcslab-nlp/simplifyingmt_acl24
공식 코드https://github.com/ntunlp/pronoun-finetuning
공식 코드https://github.com/nunpuking/end-to-end-backtranslation
공식 코드End-to-End Training for Back-Translation with Categorical Reparameterization Trick
https://github.com/nxphi47/data_diversification
공식 코드Data Diversification: A Simple Strategy For Neural Machine Translation
https://github.com/nyu-dl/dl4mt-c2c
공식 코드Fully Character-Level Neural Machine Translation without Explicit Segmentation
https://github.com/nyu-dl/dl4mt-simul-trans
공식 코드Learning to Translate in Real-time with Neural Machine Translation
https://github.com/ofirpress/YouMayNotNeedAttention
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.