https://github.com/jingtaozhan/promptreformulate
공식 코드Prompt Refinement with Image Pivot for Text-to-Image Generation
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/jinlianglu96/cl_unmt
공식 코드Exploiting Curriculum Learning in Unsupervised Neural Machine Translation
https://github.com/jinnaiyuu/mbr-optimal-transport
공식 코드Document-Level Text Generation with Minimum Bayes Risk Decoding using Optimal Transport
https://github.com/jitao-xu/dual-decoding
공식 코드One Source, Two Targets: Challenges and Rewards of Dual Decoding
https://github.com/jitao-xu/tm-levt
공식 코드Integrating Translation Memories into Non-Autoregressive Machine Translation
https://github.com/jitinkrishnan/transliteration-hindi-malayalam
공식 코드Cross-Lingual Text Classification of Transliterated Hindi and Malayalam
https://github.com/jjzha/translationese
공식 코드The Effect of Translationese in Machine Translation Test Sets
https://github.com/jlbrem/nav_robustness
공식 코드Machine Translation Robustness to Natural Asemantic Variation
https://github.com/jlfj345wf/rapo
공식 코드RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction
https://github.com/jlibovicky/char-nmt
공식 코드Towards Reasonably-Sized Character-Level Transformer NMT by Finetuning Subword Systems
https://github.com/jlibovicky/neural-string-edit-distance
공식 코드https://github.com/joelescudefont/genbiasmt
공식 코드Equalizing Gender Biases in Neural Machine Translation with Word Embeddings Techniques
https://github.com/joelmoniz/BLISS
공식 코드Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces
https://github.com/joeynmt/joeynmt
공식 코드https://github.com/johndmendonca/dialevalml
공식 코드https://github.com/johnwlambert/dlupi-heteroscedastic-dropout
공식 코드Deep Learning under Privileged Information Using Heteroscedastic Dropout
https://github.com/jonathanhus/back-to-school
공식 코드https://github.com/jonathanqzheng/neo-bench
공식 코드NEO-BENCH: Evaluating Robustness of Large Language Models with Neologisms
https://github.com/josecar25/char_based_nmt-noisy_ugc
공식 코드https://github.com/josiahwang/multisubs-eval
공식 코드MultiSubs: A Large-scale Multimodal and Multilingual Dataset
https://github.com/joyebright/da-qe-eamt2023
공식 코드Tailoring Domain Adaptation for Machine Translation Quality Estimation
https://github.com/joyebright/dataselection-nmt
공식 코드Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Texts
https://github.com/jpqiang/chinese-idiom-paraphrasing
공식 코드https://github.com/jslin/LARQS
공식 코드An Evaluation Dataset for Legal Word Embedding: A Case Study On Chinese Codex
https://github.com/juand-r/x-parade
공식 코드X-PARADE: Cross-Lingual Textual Entailment and Information Divergence across Paragraphs
https://github.com/juletx/self-translate
공식 코드https://github.com/juliakreutzer/joeynmt
공식 코드https://github.com/juliusc/bayesopt_reranking
공식 코드A Bayesian Optimization Approach to Machine Translation Reranking
https://github.com/juliusc/pruning_mbr
공식 코드Faster Minimum Bayes Risk Decoding with Confidence-based Pruning
https://github.com/jungokasai/beam_with_patience
공식 코드A Call for Clarity in Beam Search: How It Works and When It Stops
https://github.com/jungokasai/billboard
공식 코드Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand
https://github.com/jungokasai/deep-shallow
공식 코드Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation
https://github.com/jungokasai/twist_decoding
공식 코드https://github.com/jxhe/self-training-text-generation
공식 코드https://github.com/jxhe/unify-parameter-efficient-tuning
공식 코드Towards a Unified View of Parameter-Efficient Transfer Learning
https://github.com/kagi-ai/concept-unification
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.