https://github.com/google/sentencepiece
공식 코드Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates
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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/google/seq2seq
공식 코드Massive Exploration of Neural Machine Translation Architectures
https://github.com/gpengzhi/Bi-SimCut
공식 코드Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation
https://github.com/gpengzhi/CrossConST-MT
공식 코드https://github.com/gpengzhi/crossconst-llm
공식 코드Towards Boosting Many-to-Many Multilingual Machine Translation with Large Language Models
https://github.com/gpengzhi/crossconst-sr
공식 코드Learning Multilingual Sentence Representations with Cross-lingual Consistency Regularization
https://github.com/gpengzhi/simcr
공식 코드An Empirical Study of Consistency Regularization for End-to-End Speech-to-Text Translation
https://github.com/graykode/commit-autosuggestions
공식 코드CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model
https://github.com/gsarti/divemt
공식 코드DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages
https://github.com/hannibal046/nmt_with_contrastive_memories
공식 코드Neural Machine Translation with Contrastive Translation Memories
https://github.com/harvardnlp/nmt-android
공식 코드https://github.com/harvardnlp/seq2seq-attn
공식 코드https://github.com/heig-iict-ida/huffman-tokenizer
공식 코드Assessing the Importance of Frequency versus Compositionality for Subword-based Tokenization in NMT
https://github.com/hexuandeng/mono4simt
공식 코드Improving Simultaneous Machine Translation with Monolingual Data
https://github.com/howardchenhd/Syntax-awared-NMT
공식 코드Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder
https://github.com/huawei-noah/Pretrained-Language-Model
공식 코드Universal Conditional Masked Language Pre-training for Neural Machine Translation
https://github.com/hubreb/imitkd_ast
공식 코드https://github.com/i-machine-think/compositionality_paradox_mt
공식 코드The paradox of the compositionality of natural language: a neural machine translation case study
https://github.com/iampushpdeep/gender-bias-hi-en-eval
공식 코드https://github.com/ictnlp/GS4NMT
공식 코드Greedy Search with Probabilistic N-gram Matching for Neural Machine Translation
https://github.com/ictnlp/Seq-NAT
공식 코드Sequence-Level Training for Non-Autoregressive Neural Machine Translation
https://github.com/ictnlp/TLAT-NMT
공식 코드Token-level Adaptive Training for Neural Machine Translation
https://github.com/ictnlp/multiscale-contextualization
공식 코드Integrating Multi-scale Contextualized Information for Byte-based Neural Machine Translation
https://github.com/ictnlp/pluvr
공식 코드Neural Machine Translation with Phrase-Level Universal Visual Representations
https://github.com/ictnlp/su4mt
공식 코드https://github.com/idiap/HAN_NMT
공식 코드Document-Level Neural Machine Translation with Hierarchical Attention Networks
https://github.com/idiap/sense_aware_NMT
공식 코드Integrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translation
https://github.com/indian-nlp/assamese-dataset
공식 코드Enhancing Assamese NLP Capabilities: Introducing a Centralized Dataset Repository
https://github.com/ivanwang0730/styleap
공식 코드Controlling Styles in Neural Machine Translation with Activation Prompt
https://github.com/izecson/terminology-translation
공식 코드Rule-based Morphological Inflection Improves Neural Terminology Translation
https://github.com/jcyk/copyisallyouneed
공식 코드Neural Machine Translation with Monolingual Translation Memory
https://github.com/jebivid/adversarial-nmt
공식 코드On Adversarial Examples for Character-Level Neural Machine Translation
https://github.com/jeticg/Code013-CL-LexPG
공식 코드Pointer-based Fusion of Bilingual Lexicons into Neural Machine Translation
https://github.com/jiahao004/data-for-bt
공식 코드https://github.com/jiangqn/kster
공식 코드Learning Kernel-Smoothed Machine Translation with Retrieved Examples
https://github.com/jiaodali/vision-matters-when-it-should
공식 코드Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.