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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/collectivat-dev/espanyol-ladino-translation
공식 코드Preparing an Endangered Language for the Digital Age: The Case of Judeo-Spanish
https://github.com/collectivat-dev/judeo-espanyol-resources
공식 코드Preparing an Endangered Language for the Digital Age: The Case of Judeo-Spanish
https://github.com/collectivat-dev/ladino_tts
공식 코드Preparing an Endangered Language for the Digital Age: The Case of Judeo-Spanish
https://github.com/comchobo/capturing-perplexing-named-entities
공식 코드https://github.com/common-parallel-corpora/friallel
공식 코드Machine Translation for Nko: Tools, Corpora and Baseline Results
https://github.com/comp-int-hum/literary-translation
공식 코드Characterizing the Effects of Translation on Intertextuality using Multilingual Embedding Spaces
https://github.com/cone-mt/lego-mt
공식 코드Lego-MT: Learning Detachable Models for Massively Multilingual Machine Translation
https://github.com/cordercorder/nmt-multi
공식 코드Informative Language Representation Learning for Massively Multilingual Neural Machine Translation
https://github.com/craffel/mad
공식 코드Online and Linear-Time Attention by Enforcing Monotonic Alignments
https://github.com/craigastewart/qe_sim_interp
공식 코드Automatic Estimation of Simultaneous Interpreter Performance
https://github.com/csarron/MobiRNN-EMDL17
공식 코드MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU
https://github.com/csebuetnlp/banglanmt
공식 코드https://github.com/cservan/METEOR-E
공식 코드Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or Lexical Resources?
https://github.com/csikasote/bigc
공식 코드https://github.com/cuiniki/llms-are-not-scorers
공식 코드LLMs Are Not Scorers: Rethinking MT Evaluation with Generation-Based Methods
https://github.com/cyr19/reproducibility
공식 코드https://github.com/d-ataman/Char-NMT
공식 코드On the Importance of Word Boundaries in Character-level Neural Machine Translation
https://github.com/d-ataman/lmm
공식 코드A Latent Morphology Model for Open-Vocabulary Neural Machine Translation
https://github.com/dadelani/sib-200
공식 코드https://github.com/daeveraert/gradient-information-optimization
공식 코드GIO: Gradient Information Optimization for Training Dataset Selection
https://github.com/dalab/pboh-entity-linking
공식 코드https://github.com/dallascard/NLP-power-analysis
공식 코드https://github.com/danielsc4/steering-for-personalization
공식 코드Steering Large Language Models for Machine Translation Personalization
https://github.com/danifg/disco-seq2seq-parser
공식 코드https://github.com/danifg/srlpointer
공식 코드Transition-based Semantic Role Labeling with Pointer Networks
https://github.com/dannigt/attribute-controller-transfer
공식 코드How Transferable are Attribute Controllers on Pretrained Multilingual Translation Models?
https://github.com/dannigt/mid-align
공식 코드Middle-Layer Representation Alignment for Cross-Lingual Transfer in Fine-Tuned LLMs
https://github.com/dapascual/DirectedBeamSearch
공식 코드Directed Beam Search: Plug-and-Play Lexically Constrained Language Generation
https://github.com/data-lang/text-summarization-sota-experiment
공식 코드Survey on Abstractive Text Summarization: Dataset, Models, and Metrics
https://github.com/davidstap/multilingual-knn-mt
공식 코드https://github.com/dayeonki/mt_feedback
공식 코드Guiding Large Language Models to Post-Edit Machine Translation with Error Annotations
https://github.com/dayeonki/mt_quality_feedback
공식 코드https://github.com/dayeonki/rewrite_mt
공식 코드Automatic Input Rewriting Improves Translation with Large Language Models
https://github.com/dcsaunders/nmt-gender-rerank
공식 코드First the worst: Finding better gender translations during beam search
https://github.com/deep-spin/OpenNMT-APE
공식 코드A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning
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