https://github.com/formiel/fairseq
공식 코드Lightweight Adapter Tuning for Multilingual Speech Translation
Papers with Code 아카이브
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/formiel/fairseq/blob/master/examples/speech_to_text/docs/adapters.md
공식 코드Lightweight Adapter Tuning for Multilingual Speech Translation
https://github.com/franckbrl/nmt-pseudo-source-discriminator
공식 코드Using Monolingual Data in Neural Machine Translation: a Systematic Study
https://github.com/francois-meyer/ssmt
공식 코드Subword Segmental Machine Translation: Unifying Segmentation and Target Sentence Generation
https://github.com/francois-meyer/sspg
공식 코드https://github.com/francois-meyer/t2x
공식 코드https://github.com/frankkramer-lab/GERNERMED
공식 코드https://github.com/frankkramer-lab/gernermed-pp
공식 코드https://github.com/freesunshine0316/nary-grn
공식 코드Tackling Graphical NLP problems with Graph Recurrent Networks
https://github.com/freesunshine0316/semantic-nmt
공식 코드https://github.com/freewym/espresso
공식 코드Espresso: A Fast End-to-end Neural Speech Recognition Toolkit
https://github.com/frozentoad9/CMST
공식 코드Prabhupadavani: A Code-mixed Speech Translation Data for 25 Languages
https://github.com/frxxzhl/ssebt
공식 코드https://github.com/fstahlberg/ucam-scripts
공식 코드An Operation Sequence Model for Explainable Neural Machine Translation
https://github.com/fupiao1998/TrasformerSOD
공식 코드Generative Transformer for Accurate and Reliable Salient Object Detection
https://github.com/fupiao1998/transformersod
공식 코드Generative Transformer for Accurate and Reliable Salient Object Detection
https://github.com/fzp0424/ladder
공식 코드Ladder: A Model-Agnostic Framework Boosting LLM-based Machine Translation to the Next Level
https://github.com/fzp0424/mt-ladder
공식 코드Ladder: A Model-Agnostic Framework Boosting LLM-based Machine Translation to the Next Level
https://github.com/fzp0424/mt-r1-zero
공식 코드MT-R1-Zero: Advancing LLM-based Machine Translation via R1-Zero-like Reinforcement Learning
https://github.com/fzp0424/self_correct_mt
공식 코드TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement
https://github.com/g8a9/building-bridges-gender-fair-german-mt
공식 코드Building Bridges: A Dataset for Evaluating Gender-Fair Machine Translation into German
https://github.com/g8a9/gender-bias-qe-metrics
공식 코드Watching the Watchers: Exposing Gender Disparities in Machine Translation Quality Estimation
https://github.com/gabrielStanovsky/yall
공식 코드Yall should read this! Identifying Plurality in Second-Person Personal Pronouns in English Texts
https://github.com/galpatel/minimal-paraphrases
공식 코드On Neurons Invariant to Sentence Structural Changes in Neural Machine Translation
https://github.com/galsang/SemEval2018-task12
공식 코드https://github.com/george0828zhang/sinkhorn-simultrans
공식 코드Anticipation-Free Training for Simultaneous Machine Translation
https://github.com/georgevern/smala
공식 코드https://github.com/gguinet/semisupervised-alignement
공식 코드https://github.com/gguinet/semisupervised-alignment
공식 코드https://github.com/ghazi-f/advae
공식 코드Towards Unsupervised Content Disentanglement in Sentence Representations via Syntactic Roles
https://github.com/ghchen18/acl22-sixtp
공식 코드Towards Making the Most of Multilingual Pretraining for Zero-Shot Neural Machine Translation
https://github.com/ghchen18/emnlp2021-sixt
공식 코드Zero-shot Cross-lingual Transfer of Neural Machine Translation with Multilingual Pretrained Encoders
https://github.com/ghrua/ccpr_release
공식 코드https://github.com/ghrua/ngramres
공식 코드$N$-gram Is Back: Residual Learning of Neural Text Generation with $n$-gram Language Model
https://github.com/gianlucavico/larth-etruscan-nlp
공식 코드https://github.com/gipplab/cl-osa
공식 코드Detecting Cross-Language Plagiarism using Open Knowledge Graphs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.