https://github.com/gringham/bmx
공식 코드BMX: Boosting Natural Language Generation Metrics with Explainability
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/gringham/explainable-metrics-machine-translation
공식 코드Towards Explainable Evaluation Metrics for Natural Language Generation
https://github.com/gringham/prexme
공식 코드https://github.com/gsarti/divemt
공식 코드DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages
https://github.com/gsarti/labl
공식 코드https://github.com/gsarti/pecore
공식 코드Quantifying the Plausibility of Context Reliance in Neural Machine Translation
https://github.com/gsarti/qe4pe
공식 코드https://github.com/guangyliu/EISL
공식 코드Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation
https://github.com/guanleics/pipeoptim
공식 코드PipeOptim: Ensuring Effective 1F1B Schedule with Optimizer-Dependent Weight Prediction
https://github.com/h-j-han/VocADT
공식 코드Adapters for Altering LLM Vocabularies: What Languages Benefit the Most?
https://github.com/h-j-han/speechqe
공식 코드SpeechQE: Estimating the Quality of Direct Speech Translation
https://github.com/haantran96/wavetransformer
공식 코드https://github.com/hainow/MCTN
공식 코드https://github.com/halfrot/ALaRM
공식 코드ALaRM: Align Language Models via Hierarchical Rewards Modeling
https://github.com/hangeol/unir
공식 코드Universal Reasoner: A Single, Composable Plug-and-Play Reasoner for Frozen LLMs
https://github.com/hannibal046/nmt_with_contrastive_memories
공식 코드Neural Machine Translation with Contrastive Translation Memories
https://github.com/hannibal046/selfmemory
공식 코드Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory
https://github.com/haorannlp/mix
공식 코드https://github.com/haroonshakeel/bilingual_sms_classification
공식 코드A Multi-cascaded Deep Model for Bilingual SMS Classification
https://github.com/harshildarji/thesis
공식 코드https://github.com/harshvivek14/RoundTripOCR
공식 코드https://github.com/harvardnlp/BSO
공식 코드https://github.com/harvardnlp/cascaded-generation
공식 코드https://github.com/harvardnlp/nmt-android
공식 코드https://github.com/harvardnlp/seq2seq-attn
공식 코드https://github.com/harvardnlp/var-attn
공식 코드https://github.com/haryoa/stif-indonesia
공식 코드https://github.com/hassyGo/NLG-RL
공식 코드Accelerated Reinforcement Learning for Sentence Generation by Vocabulary Prediction
https://github.com/hclent/creoleval
공식 코드https://github.com/hecta-uom/clinicalnmt
공식 코드https://github.com/heidelkin/BIPNMT
공식 코드A Reinforcement Learning Approach to Interactive-Predictive Neural Machine Translation
https://github.com/heig-iict-ida/huffman-tokenizer
공식 코드Assessing the Importance of Frequency versus Compositionality for Subword-based Tokenization in NMT
https://github.com/helsinki-nlp/mammoth
공식 코드MAMMOTH: Massively Multilingual Modular Open Translation @ Helsinki
https://github.com/helsinki-nlp/opus-mt
공식 코드https://github.com/hemingkx/gad
공식 코드Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation
https://github.com/hemingkx/specdec
공식 코드Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.