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/cerebroai/AskIt
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/chakravarthi-v/Polaroid-1
https://github.com/chalothon/BERT_Practice
https://github.com/chancefocus/pixiu
FinBen: A Holistic Financial Benchmark for Large Language Models
https://github.com/chandu7077/mybert
https://github.com/chaneeh/SeqGAN_experiment
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://github.com/chanshing/sobolev_gan
https://github.com/chao-ji/tf-seq2seq
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/chen-gx/toolevo
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
https://github.com/chen-xiong-yi/OwnBERT
https://github.com/chengchingwen/transformers.jl
https://github.com/chiayewken/bert-qa
https://github.com/chirag2796/RNN-LSTM-for-Text-Generation
Context-aware Natural Language Generation with Recurrent Neural Networks
https://github.com/chrisleunglokhin/Capstone-BERT
https://github.com/christianspybrook/article_recommender
https://github.com/chung771026/Implement-seqGAN-with-Keras
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://github.com/chunghyunhee/twitter_disaster_NLP
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/chunlei2/show-and-tell
https://github.com/ciaua/HandwritingPyTorch
https://github.com/cindyxinyiwang/TextStyleTransfer
A Probabilistic Formulation of Unsupervised Text Style Transfer
https://github.com/cindyxinyiwang/fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/circlePi/knowledge-driven-dialogue-lic2019
Multiple Generative Models Ensemble for Knowledge-Driven Proactive Human-Computer Dialogue Agent
https://github.com/ckzbullbullet/TP-N2F
https://github.com/clip-italian/clip-italian
https://github.com/clock-me/text-restyle
https://github.com/cloudmcloudyo/capstone
https://github.com/cmsflash/efficient-attention
https://github.com/coco60/bert-test
https://github.com/codecaution/evomoe
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/codehacpj/Image_description
https://github.com/codelion/adaptive-classifier
https://github.com/cody2007/arcane_fortune
https://github.com/coldmist-lu/erroranalysis_prompt
Toward Human-Like Evaluation for Natural Language Generation with Error Analysis
https://github.com/colorfulscoop/tfdlg
https://github.com/conceptofmind/LaMDA-pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.