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/nfelnlp/nfelnlp
Inseq: An Interpretability Toolkit for Sequence Generation Models
https://github.com/ngocphucck/BERT-questions-classification
https://github.com/niccolot/ENG-ITA_NMT
https://github.com/nicholasbao/nlp_job
https://github.com/nicolafan/image-captioning-cnn-rnn
https://github.com/nietao2/DeepSpeechRecognition
https://github.com/nightdessert/WeCheck/blob/main/README.md
WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learning
https://github.com/nikhilmaram/Show_and_Tell
https://github.com/ninodimontalcino/moralchoice
https://github.com/nishimehta/Image_Captioning
https://github.com/nithishkaviyan/Show-and-Tell-Neural-Network-Image-Caption-Generator-
https://github.com/niyunsheng/ems-sd
https://github.com/nlahlaf/Text-Style-Transfer
https://github.com/nlinc1905/dsilt-tsa
https://github.com/nlmatics/llmsherpa
https://github.com/nlp-detoxify/fudge-controlled-generation
FUDGE: Controlled Text Generation With Future Discriminators
https://github.com/nmfisher/bert-modified
https://github.com/nopperl/clip_arxiv_pmc
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/noriyukipy/gptchat
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/noriyukipy/tfchat
https://github.com/noriyukipy/tfdlg
https://github.com/nota-netspresso/shortened-llm
LLM-Pruner: On the Structural Pruning of Large Language Models
https://github.com/nrc-cnrc/sockeye-multisource
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/nsfzyzz/generalization_metrics_for_nlp
https://github.com/nurpeiis/LeakGAN-PyTorch
Long Text Generation via Adversarial Training with Leaked Information
https://github.com/nvshrao/AlignAndTranslate
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/nvshrao/Bidirectional-Transformer
https://github.com/nyu-dl/dl4dial-bayesian-calibration
Importance of Search and Evaluation Strategies in Neural Dialogue Modeling
https://github.com/oarriaga/neural_image_captioning
https://github.com/obss/disgem
DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking
https://github.com/okkteam/Transformer-Transducer
https://github.com/oleehyo/texteller
TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
https://github.com/ololo123321/maestro
https://github.com/oneTimePad/conv-nmt
https://github.com/open-finance-lab/finlora
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.