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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/zhengzx-nlp/past-and-future-nmt
공식 코드https://github.com/zhongxingyu/seer
공식 코드Learning the Relation between Code Features and Code Transforms with Structured Prediction
https://github.com/zhouh/chunk-nmt
공식 코드https://github.com/zippotju/Exposure-Bias-Hallucination-Domain-Shift
공식 코드On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translation
https://github.com/zjersey/lightseq-arm
공식 코드https://github.com/zjwang21/strokenet
공식 코드https://github.com/zouharvi/LeverageAlign
공식 코드https://github.com/zurichnlp/nmtscore
공식 코드NMTScore: A Multilingual Analysis of Translation-based Text Similarity Measures
https://github.com/zurichnlp/translation-direction-detection
공식 코드Machine Translation Models are Zero-Shot Detectors of Translation Direction
https://github.com/zzsfornlp/znmt-merge
공식 코드Exploring Recombination for Efficient Decoding of Neural Machine Translation
https://gitlab.com/skalo/baroque-nmt
공식 코드Modeling Baroque Two-Part Counterpoint with Neural Machine Translation
https://github.com/1-punchMan/CLTS
https://github.com/2023-MindSpore-1/ms-code-5/tree/main/gnmt_v2
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/A-Jacobson/minimal-nmt
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/AI-coder/SEQ2SEA
Massive Exploration of Neural Machine Translation Architectures
https://github.com/AI4Bharat/indicTrans
Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages
https://github.com/Attn-to-FC/Attn-to-FC
code2seq: Generating Sequences from Structured Representations of Code
https://github.com/Automattic/wp-translate
https://github.com/Avmb/code-docstring-corpus
https://github.com/BigBinnie/Benchmarking-LLM-based-Machine-Translation-on-Cultural-Awareness
https://github.com/ChaiBapchya/mxnet-hardmax_machine_translation
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/CongBao/ChatBot
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/DorinK/Assignment-2-Neural-Machine-Translation
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/EdinburghNLP/code-docstring-corpus
https://github.com/Flajt/Veritas
https://github.com/FlorianPfisterer/2D-LSTM-Seq2Seq
Towards Two-Dimensional Sequence to Sequence Model in Neural Machine Translation
https://github.com/FreddeFrallan/Multilingual-CLIP
https://github.com/Glaceon31/Document-Transformer
THUMT: An Open Source Toolkit for Neural Machine Translation
https://github.com/Gldkslfmsd/t2t_second
Morphological and Language-Agnostic Word Segmentation for NMT
https://github.com/HarshKhandelwal1552/Language_classifier_with_Naive_Bayes
https://github.com/Helsinki-NLP/OpenNMT-py
https://github.com/Helsinki-NLP/shared-info
https://github.com/Henry-E/OpenNMT-py
https://github.com/Izecson/saml-nmt
https://github.com/Izecson/sockeye-1.16.6
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.