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/EugeneSel/EUMT
https://github.com/EuphoriaYan/ChatRobot-For-Keras2
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/EuphoriaYan/bert_component
https://github.com/FKIRSTE/emnlp2024-personalized-meeting-sum
https://github.com/FaceOnLive/ID-Card-Passport-Recognition-SDK-Android
Data Generation for Post-OCR correction of Cyrillic handwriting
https://github.com/FaskyCC/TextClassification
https://github.com/Felixgithub2017/MMCU
Evaluating the Generation Capabilities of Large Chinese Language Models
https://github.com/FengJiaChunFromSYSU/Bert
https://github.com/FlorianPfisterer/2D-LSTM-Seq2Seq
https://github.com/FreddeFrallan/Multilingual-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/FreeCheng523/Task-of-Text-Generation
https://github.com/Fuchai/language
https://github.com/FudanNLP/Irl_gen
Toward Diverse Text Generation with Inverse Reinforcement Learning
https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction
https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction-roBERTa-5fold
https://github.com/Gahyeonkim09/AAPL
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/Gaozhen0816/BERT_QA_For_AILaw
https://github.com/Gaozhen0816/BERT_QA_for_Chinese
https://github.com/Glaceon31/Document-Transformer
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/GlassyWing/transformer-keras
https://github.com/Gowthamkar6/Image-captioning-using-CNN-RNN
https://github.com/GuessWhatGame/referit
https://github.com/Guillem96/pointer-nn-pytorch
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/GuyTevet/rnn-gan-eval
Language Generation with Recurrent Generative Adversarial Networks without Pre-training
https://github.com/HEIMaxER/descript-research-test
https://github.com/HHousen/TransformerSum
https://github.com/HKUNLP/Dream
Structured Denoising Diffusion Models in Discrete State-Spaces
https://github.com/HKUST-KnowComp/R-Net
https://github.com/HMJiangGatech/dialogue_ope_data
What makes a good conversation? How controllable attributes affect human judgments
https://github.com/HanhaiNotHai/transformer
https://github.com/Harry-Muzart/harry-muzart.github.io
https://github.com/HazyResearch/prefix-linear-attention
Simple linear attention language models balance the recall-throughput tradeoff
https://github.com/Helsinki-NLP/OpenNMT-py
https://github.com/Helsinki-NLP/shared-info
https://github.com/HemaDevaSagar35/NeuralMachineTranslation-French2English
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.