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/UKPLab/controlled-argument-generation
CTRL: A Conditional Transformer Language Model for Controllable Generation
https://github.com/UKPLab/e2e-nlg-challenge-2017
https://github.com/UTMIST/Humerus
GLTR: Statistical Detection and Visualization of Generated Text
https://github.com/UdbhavPrasad072300/Transformer-Implementation
https://github.com/UdbhavPrasad072300/Transformer-Implementation-and-Language-Translation
https://github.com/UdbhavPrasad072300/Transformer-Implementations
https://github.com/VAShibaev/text_style_transfer
Style Transfer for Texts: Retrain, Report Errors, Compare with Rewrites
https://github.com/VachanVY/gpt.jax
https://github.com/VedantYadav/Transformer
https://github.com/VegB/VLN-Transformer
Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation
https://github.com/Vergangenheit/Conv-AI
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/VikhrModels/effective_llm_alignment
https://github.com/VinitSR7/Image-Caption-Generation
https://github.com/Vrushank264/Handwriting-Generation-using-LSTM
https://github.com/W4ngatang/qags
https://github.com/Walter-B/bert-20-classes
https://github.com/WangZesen/GAN-Hinge-Loss
https://github.com/WangZesen/Spectral-Normalization-GAN
https://github.com/WebQnA/WebQA_Baseline
Unified Vision-Language Pre-Training for Image Captioning and VQA
https://github.com/WenYanger/General-Transformer-Pytorch
https://github.com/Wence-May/Image-Annotation
https://github.com/WenjieDu/PyPOTS
https://github.com/WenqiJiang/SC-ANN-FPGA
https://github.com/WhiskyChoy/language-style-transfer
https://github.com/WolfNiu/AdversarialDialogue
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
https://github.com/WolfNiu/polite-dialogue-generation
https://github.com/Wolfie8935/Implementation-of-Attention-is-all-you-need
https://github.com/XINZXZHANG/Bert_repo
https://github.com/XUJitao/WordEmbedding
https://github.com/XapaJIaMnu/transformative
https://github.com/Xinsen-Zhang/transformer
https://github.com/XuhanLiu/Transformer
https://github.com/YYGXjpg/BERT_WL
https://github.com/Yifan-Gao/Distractor-Generation-RACE
Generating Distractors for Reading Comprehension Questions from Real Examinations
https://github.com/Yipeng91/text_classifier_pub
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.