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/VladimirV99/Automatic-Translation
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/Waino/OpenNMT-py
https://github.com/Waino/hnmt
Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models
https://github.com/Xinsen-Zhang/transformer
https://github.com/YuweiYin/GTrans
Training Deeper Neural Machine Translation Models with Transparent Attention
https://github.com/YuweiYin/HLT-MT
Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation
https://github.com/ZHANG45/multi_gpu_seq2seq
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/ZNLP/sb-nmt
https://github.com/ZanzaKlaus/DeepTranslate
Massive Exploration of Neural Machine Translation Architectures
https://github.com/ZhenYangIACAS/NMT
https://github.com/aizhanti/jarunc
https://github.com/alanjia163/first1
Massive Exploration of Neural Machine Translation Architectures
https://github.com/alankarj/robust_nlp
On Adversarial Examples for Character-Level Neural Machine Translation
https://github.com/amitz25/PCCoder
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/anhtu-phan/multi-domain-machine-translation
Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing
https://github.com/anonymouscode115/autoregressive_inference
https://github.com/antonisa/lang2vec
https://github.com/aodongli/LearningFromDraft
https://github.com/aodongli/Learning_from_draft
https://github.com/artetxem/phrase2vec
https://github.com/as-ideas/TransformerTTS
https://github.com/atpaino/deep-text-corrector
Addressing the Rare Word Problem in Neural Machine Translation
https://github.com/b-etienne/Seq2seq-PyTorch
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/benjaminbeilharz/hierarchical-reinforcement-learning
https://github.com/biyoml/End-to-End-Mandarin-ASR
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/biyoml/Pytorch-End-to-End-ASR-on-TIMIT
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/blickwinkel1107/making-the-most-of-context-nmt
Towards Making the Most of Context in Neural Machine Translation
https://github.com/bojone/labse
https://github.com/bplank/teaching-dl4nlp
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/braindecode/braindecode
Amplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer Learning
https://github.com/caesium94/seq2seq_fix
Massive Exploration of Neural Machine Translation Architectures
https://github.com/cairouchong/discourse-phenomena
Pronoun Translation in English-French Machine Translation: An Analysis of Error Types
https://github.com/chao-ji/tf-seq2seq
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/choiHkk/Transformer-TTS
https://github.com/cisnlp/simalign
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.