https://github.com/linzhiqiu/t2i_metrics
공식 코드Evaluating Text-to-Visual Generation with Image-to-Text Generation
Papers with Code 아카이브
Text Generation
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Text Generation is the task of generating text with the goal of appearing indistinguishable to human-written text. This task is more formally known as "natural language generation" in the literature.
Text generation can be addressed with Markov processes or deep generative models like LSTMs. Recently, some of the most advanced methods for text generation include BART, GPT and other GAN-based approaches. Text generation systems are evaluated either through human ratings or automatic evaluation metrics like METEOR, ROUGE, and BLEU.
Further readings:
<span style="color:grey; opacity: 0.6">( Image credit: Adversarial Ranking for Language Generation )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/linzhiqiu/t2v_metrics
공식 코드Evaluating Text-to-Visual Generation with Image-to-Text Generation
https://github.com/lipiji/Guyu
공식 코드https://github.com/lipiji/SongNet
공식 코드https://github.com/lisk123/uccr
공식 코드User-Centric Conversational Recommendation with Multi-Aspect User Modeling
https://github.com/litterbrother-xiao/overview-of-non-autoregressive-applications
공식 코드A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond
https://github.com/littlehacker26/discriminator-cooperative-unlikelihood-prompt-tuning
공식 코드DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text Generation
https://github.com/littlehacker26/residual_memory_transformer
공식 코드Controllable Text Generation with Residual Memory Transformer
https://github.com/littlekobe/AREL
공식 코드No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling
https://github.com/liuchengyuan123/l2tviacounterfactualsamples
공식 코드https://github.com/liuyuxiang512/repa
공식 코드Writing Like the Best: Exemplar-Based Expository Text Generation
https://github.com/liuzuyan/elasticcache
공식 코드Efficient Inference of Vision Instruction-Following Models with Elastic Cache
https://github.com/liyongqi67/ltrgr
공식 코드https://github.com/liyucheng09/contamination_detector
공식 코드https://github.com/liyucheng09/metaphor_generator
공식 코드https://github.com/liyzcj/seggan
공식 코드https://github.com/ljm565/tesgan
공식 코드https://github.com/ljyflores/filipino-slang
공식 코드https://github.com/lkwate/neural-question-generation
공식 코드Semantic-Based Self-Critical Training For Question Generation
https://github.com/long21wt/robust-bert-based-metrics
공식 코드Layer or Representation Space: What makes BERT-based Evaluation Metrics Robust?
https://github.com/longfeiyun17/diversitycollapse
공식 코드https://github.com/lorenzkuhn/semantic_uncertainty
공식 코드https://github.com/lou1sm/fastfood
공식 코드The Proof is in the Pudding: Using Automated Theorem Proving to Generate Cooking Recipes
https://github.com/lovodkin93/attribute-first-then-generate
공식 코드Attribute First, then Generate: Locally-attributable Grounded Text Generation
https://github.com/lovodkin93/cdr_ctr
공식 코드Dont Add, dont Miss: Effective Content Preserving Generation from Pre-Selected Text Spans
https://github.com/lowlypalace/global-decoding
공식 코드https://github.com/lsy641/expectation-adjusted-distinct
공식 코드https://github.com/ltrc/mukhyansh
공식 코드Mukhyansh: A Headline Generation Dataset for Indic Languages
https://github.com/ltrc/teclass
공식 코드TeClass: A Human-Annotated Relevance-based Headline Classification and Generation Dataset for Telugu
https://github.com/luffy06/refusion
공식 코드https://github.com/luka-group/lattice
공식 코드Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning
https://github.com/lukaborec/memorization-nucleus-sampling
공식 코드The Unreasonable Ineffectiveness of Nucleus Sampling on Mitigating Text Memorization
https://github.com/luofuli/DualLanST
공식 코드A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer
https://github.com/lvapeab/ABiViRNet
공식 코드Video Description using Bidirectional Recurrent Neural Networks
https://github.com/lvyiwei1/StylePTB
공식 코드StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer
https://github.com/lvyiwei1/diffuseq_styleptb
공식 코드Fine-grained Text Style Transfer with Diffusion-Based Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.