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/2024-MindSpore-1/Code2/tree/main/model-1/biogpt
BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/blip_2
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/ctrl
CTRL: A Conditional Transformer Language Model for Controllable Generation
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/gpt2
SP-GPT2: Semantics Improvement in Vietnamese Poetry Generation
https://github.com/2024-MindSpore-1/Code4/tree/main/DeepText
https://github.com/2miatran/Natural-Language-Processing
https://github.com/4ai/bellm
https://github.com/A-Jacobson/minimal-nmt
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/ADGEfficiency/dsr_rl
https://github.com/AIJoris/DPAC-DialogueGAN
https://github.com/AIPHES/emnlp19-moverscore
MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance
https://github.com/AMNAALMGLY/NLP
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/AWLyrics/SeqGAN_Poem
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://github.com/AaronCCWong/Show-Attend-and-Tell
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/AdarshKumar712/PPLM.jl
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
https://github.com/Advancing-Machine-Human-Reasoning-Lab/apt
https://github.com/AjayKhalsa/convai_smile
Personalizing Dialogue Agents: I have a dog, do you have pets too?
https://github.com/Akella17/Handwriting_Synthesis
https://github.com/AlCorreia/FABIR
https://github.com/Amy-1114/transformer
https://github.com/AndreasInk/Quiz-APIv2
Simplifying Paragraph-level Question Generation via Transformer Language Models
https://github.com/AndreiMoraru123/ContextCollector
https://github.com/AndreiMoraru123/Neural-Machine-Translation
https://github.com/AndreiMoraru123/Watch-and-Tell
https://github.com/AndresPMD/Clip_CMR
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/Andrey885/Machine_translation_PyTorch
https://github.com/AnesBenmerzoug/Handwriting-Model
https://github.com/AniketBajpai/deep-handwriting-generation
https://github.com/Anirudh-Phukan/verifiability-granular
https://github.com/AnirudhMukherjee/story-generation
https://github.com/Anjaney1999/image-captioning-seqgan
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://github.com/Ankur3107/awesome-daily-blog
TextKD-GAN: Text Generation using KnowledgeDistillation and Generative Adversarial Networks
https://github.com/AnuragBalsaraf/ERC
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
https://github.com/ArdalanM/nlp-benchmarks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.