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/HomebrewNLP/HomebrewNLP-Jax/blob/main/inference.py
https://github.com/HomuraNagato/NLP_finetune
https://github.com/HzcIrving/DeepLearning_PlayGround
https://github.com/IBM/pytorch-seq2seq
https://github.com/IMvision12/keras-vision-models
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/INK-USC/CommonGen
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning
https://github.com/IOB-Muenster/TEclass2
https://github.com/IS5882/Open-CyKG
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/ImadENSMN/pplm_gptneo
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
https://github.com/IpastorSan/seq2seq-with-attention-OCR-translation
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/IrishCoffee/cudnnMultiHeadAttention
https://github.com/Iwontbecreative/Abstractive-summarization-OpenNMT
https://github.com/Izecson/saml-nmt
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/Izecson/sockeye-1.16.6
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/JHKim-snu/GVCCI
https://github.com/JHKim-snu/PGA
https://github.com/JNUpython/bert
https://github.com/JRC1995/Abstractive-Summarization
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/JRC1995/Machine-Translation-Transformers
https://github.com/JanAthmer/Compositional-generalization-capabillity-of-Transformer
https://github.com/Japanese-Image-Captioning/M2-transformer-for-Japanese
https://github.com/JasonZhu-WHU/CoderAssistant
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
https://github.com/JeheonPark596/sentimentsurvival
https://github.com/Jiahao-Yuan/Reversal-of-Thought
https://github.com/JoegameZhou/mPanGu-Alpha-53
https://github.com/JohannLee1996/bert
https://github.com/JohnKurian/TableGPT
https://github.com/Joshuaoneheart/Fintech-Final-Report
https://github.com/Julie260/DeepLearningProject
https://github.com/JunnYu/paddle_ctrl
CTRL: A Conditional Transformer Language Model for Controllable Generation
https://github.com/Jupaoqq/Jupaoqq_LaRL
https://github.com/Jxu-Thu/DIITTO
Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation
https://github.com/KaijuML/parent
Handling Divergent Reference Texts when Evaluating Table-to-Text Generation
https://github.com/KaijuML/rotowire-rg-metric
https://github.com/Kakoedlinnoeslovo/fairseq
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.