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/YouzhiTian/MOSS-End-to-End-Dialog-System-Framework-with-Modular-Supervision
MOSS: End-to-End Dialog System Framework with Modular Supervision
https://github.com/YuanEric88/bert-nlu
https://github.com/Yuancheng-Xu/GenARM
GenARM: Reward Guided Generation with Autoregressive Reward Model for Test-time Alignment
https://github.com/YufanPaPa/GAN_SSN
https://github.com/YukiDaSlayer316/R-Tutorial
https://github.com/YunwenTechnology/Unilm
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/YvanG/VQGAN-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/YvesWang/Machine_Translation_NLP
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/ZHEvent/ZHEvent.github.io
https://github.com/ZackPashkin/text2cartoon-pytorch-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/Zefty/rag-end2end-retriever
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/Zehui127/SQUAD_BERT
https://github.com/Zeng-WH/PrefixTuning-Fix
https://github.com/ZhouYC627/NN_Compression
https://github.com/ZmeiGorynych/transformer_pytorch
https://github.com/ZurichNLP/sockeye
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/a414351664/Bert_MRC
https://github.com/a414351664/my_git_laser
https://github.com/a736875071/clip-vit-large-patch14
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/aaaceo890/Attention
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/aananda-giri/gpt2-nepali
https://github.com/aaronH45/sentimentAnalysis
https://github.com/aashutj/Multimodal-Multiparty-Emotion-Recognition-System-in-Conversation-Text-Mode-Only-
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
https://github.com/abhaskumarsinha/Corpus2GPT
https://github.com/abhaskumarsinha/Keras-implementation-of-Transformer-Architecture
https://github.com/abhaskumarsinha/Keras-implementation-of-Transformer-Architecture/blob/main/Transformer.py
https://github.com/abhaskumarsinha/MinimalGPT
https://github.com/abhaskumarsinha/Seq2Seq-Bahdanau-Attention-based-Encoder-Decoder-Language-Translator
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/abhiagg92/bert_sentiment
https://github.com/abhilashreddys/Fake-News-Article
https://github.com/abhishekr7/Summarization-of-Radiological-Reports
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/abhishekshakya/seq-2-seq-for-neural-machine-translation-english-to-hindi-
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/abmitra84/Machine_Translation
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/adbrebs/handwriting
https://github.com/adi2103/AML-CoVe
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.