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/Kaushalya/medclip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/KeithGalli/pycon2020
https://github.com/Kevin-Vora/bert-embedding-gluonnlp-edit-
https://github.com/KevinYoung98/Seq2Seq-PyTorch
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/Khalid-Jamal/annotated-transformer-Arabic
https://github.com/KhueNguyen312/Persona-Chatbot
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/Kickflip89/NLP_Project
https://github.com/Kirankumaraswamy/Transformers
https://github.com/KnightZhang625/BERT_TF
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/KrishnaTarun/Deep-Learning-Lab
https://github.com/KurtAhn/lyrebird
https://github.com/KushGrandhi/Polaroid
https://github.com/Kyubyong/transformer
https://github.com/L0SG/seqgan-music
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://github.com/LanceZPF/OpenING
OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation
https://github.com/Lemoners/Attention-Is-All-You-Need
https://github.com/LeoWood/bert-horovod
https://github.com/LeonZh0u/Chatbot
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/LiamMaclean216/Pytorch-Chatbot
https://github.com/LiamMaclean216/Pytorch-Transfomer
https://github.com/LiangqunLu/DLForChatbot
https://github.com/LivXue/MMT
https://github.com/LoveYang/bert_test
https://github.com/Lsdefine/attention-is-all-you-need-keras
https://github.com/MCLYang/RhythmGAN_pytorch
https://github.com/MOHAMEDELDAKDOUKY/bert_adjusted
https://github.com/MS-P3/code4/tree/main/TAT
TaTa: A Multilingual Table-to-Text Dataset for African Languages
https://github.com/MS-P3/code5/tree/main/gpt2
https://github.com/MaZhiyuanBUAA/bert-tf1.4.0
https://github.com/Maab-Nimir/Neural-Machine-Translation-by-Jointly-Learning-to-Align-and-Translate
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/Mandar-Sharma/TCube
https://github.com/Maple728/transformer
https://github.com/MarcBS/keras
https://github.com/MashaKubyshina/learning_ML
https://github.com/Matthewdowney18/Transformer_Dialogue
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.