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/suragnair/seqGAN
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://github.com/suryachintu/Quora-Insincere-Questions-Kaggle
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/svakulenk0/response_eval
https://github.com/swechhachoudhary/Handwriting-synthesis
https://github.com/swimmingCreative/DeepSpeechRecognition-master
https://github.com/swordelucidator/nanobackpacklm
https://github.com/sy-sunmoon/Clever-Commenter-Let-s-Try-More-Apps
https://github.com/szcom/rnnlib
https://github.com/tagoyal/factuality-datasets
https://github.com/taited/clip-score
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/tamsiuhin/oppu
https://github.com/tanjeffreyz/attention-is-all-you-need
https://github.com/tanyuqian/aspect-based-summarization
https://github.com/tanyuqian/progressive-generation
Toward Diverse Text Generation with Inverse Reinforcement Learning
https://github.com/tanyuqian/texar-pytorch
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
https://github.com/tatp22/linformer-pytorch
https://github.com/tatp22/multidim-positional-encoding
https://github.com/tatsu-lab/linguistic_calibration
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
https://github.com/tbmoon/LANL_Earthquake_Prediction
https://github.com/tcnguyen/bert
https://github.com/tcxdgit/MachineTranslation_t2t
https://github.com/tedmoskovitz/constrainedrl4lms
https://github.com/tensorops/TransformerX
https://github.com/terrierteam/pyterrier_rag
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
https://github.com/teticio/llama-squad
https://github.com/text-machine-lab/transformerpy
https://github.com/tfolkman/deep-learning-experiments
https://github.com/tgao1337/detecting-fake-text
GLTR: Statistical Detection and Visualization of Generated Text
https://github.com/tgao1337/detecting-fake-text-2
GLTR: Statistical Detection and Visualization of Generated Text
https://github.com/thanhlong1997/bert_quora
https://github.com/the-Quert/iNLPfun
https://github.com/the-pythoncoder/councelChatbot2
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/the-pythoncoder/counsel-chat
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/theQuert/inlpfun
https://github.com/theamrzaki/text_summurization_abstractive_methods
Neural Machine Translation by Jointly Learning to Align and Translate
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.