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Machine Translation
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Machine translation is the task of translating a sentence in a source language to a different target language.
Approaches for machine translation can range from rule-based to statistical to neural-based. More recently, encoder-decoder attention-based architectures like BERT have attained major improvements in machine translation.
One of the most popular datasets used to benchmark machine translation systems is the WMT family of datasets. Some of the most commonly used evaluation metrics for machine translation systems include BLEU, METEOR, NIST, and others.
<span style="color:grey; opacity: 0.6">( Image credit: Google seq2seq )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/nunpuking/end-to-end-backtranslation
공식 코드End-to-End Training for Back-Translation with Categorical Reparameterization Trick
https://github.com/nusnlp/PtrNetDecoding4JERE
공식 코드Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction
https://github.com/nusnlp/c2e-mt-benchmark
공식 코드Upping the Ante: Towards a Better Benchmark for Chinese-to-English Machine Translation
https://github.com/nverma1/merging-ffs-compression
공식 코드https://github.com/nxphi47/data_diversification
공식 코드Data Diversification: A Simple Strategy For Neural Machine Translation
https://github.com/nxphi47/multiagent_crosstranslate
공식 코드Cross-model Back-translated Distillation for Unsupervised Machine Translation
https://github.com/nxphi47/refine_unsup_multilingual_mt
공식 코드Refining Low-Resource Unsupervised Translation by Language Disentanglement of Multilingual Model
https://github.com/nyu-dl/NLP_DL_Lecture_Note
공식 코드Natural Language Understanding with Distributed Representation
https://github.com/nyu-dl/dl4mt-c2c
공식 코드Fully Character-Level Neural Machine Translation without Explicit Segmentation
https://github.com/nyu-dl/dl4mt-cdec
공식 코드Plan, Attend, Generate: Character-level Neural Machine Translation with Planning in the Decoder
https://github.com/nyu-dl/dl4mt-nonauto
공식 코드Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement
https://github.com/nyu-dl/dl4mt-seqgen
공식 코드A Generalized Framework of Sequence Generation with Application to Undirected Sequence Models
https://github.com/nyu-dl/dl4mt-simul-trans
공식 코드Learning to Translate in Real-time with Neural Machine Translation
https://github.com/oceanypt/TOA
공식 코드https://github.com/ohlionel/Prune-Tune
공식 코드Finding Sparse Structures for Domain Specific Neural Machine Translation
https://github.com/omidkashefi/Mizan
공식 코드https://github.com/open-speech-ekstep/indic-punct
공식 코드https://github.com/openai/gpt-3
공식 코드https://github.com/openscilab/parsipy
공식 코드https://github.com/oplatek/reprohum-as-little-as-possible
공식 코드With a Little Help from the Authors: Reproducing Human Evaluation of an MT Error Detector
https://github.com/orangeinsouth/challengewmt
공식 코드Aligning Translation-Specific Understanding to General Understanding in Large Language Models
https://github.com/orangeinsouth/lssd
공식 코드Unifying the Convergences in Multilingual Neural Machine Translation
https://github.com/orangeinsouth/pareto-mutual-distillation
공식 코드Towards Higher Pareto Frontier in Multilingual Machine Translation
https://github.com/orevaahia/mc4lrnmt
공식 코드The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation
https://github.com/orevaahia/yorulect
공식 코드Voices Unheard: NLP Resources and Models for Yorùbá Regional Dialects
https://github.com/orevaoghene/pidgin-baseline
공식 코드Towards Supervised and Unsupervised Neural Machine Translation Baselines for Nigerian Pidgin
https://github.com/orionw/multilingual-federated-learning
공식 코드https://github.com/orrp/theory-of-umt
공식 코드A Theory of Unsupervised Translation Motivated by Understanding Animal Communication
https://github.com/owennju/ink
공식 코드INK: Injecting kNN Knowledge in Nearest Neighbor Machine Translation
https://github.com/owennju/mmt-llm
공식 코드Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis
https://github.com/pangjh3/llm4mt
공식 코드Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models
https://github.com/pardissz/translate-with-care
공식 코드https://github.com/pawel-m/context-mt-attention-analysis
공식 코드Analyzing the Attention Heads for Pronoun Disambiguation in Context-aware Machine Translation Models
https://github.com/pemywei/MSC-NMT
공식 코드Multiscale Collaborative Deep Models for Neural Machine Translation
https://github.com/pemywei/csanmt
공식 코드Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation
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