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/ArvidWartenberg/retrosynthesis
https://github.com/AsianZeus/Ooze-Handwritten-Text-Generator
https://github.com/AssafSinger94/sigmorphon-2020-inflection
https://github.com/AtluriNikhil/Image-Captioning
https://github.com/Attn-to-FC/Attn-to-FC
Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks
https://github.com/AuCson/SEDST
Explicit State Tracking with Semi-Supervision for Neural Dialogue Generation
https://github.com/Aveek-Saha/Transformer
https://github.com/BPYap/IMT-Style-Transfer
IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation
https://github.com/BSlience/end2end-conversational-ai
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/BachOzean/TadE
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
https://github.com/Baichenjia/NMT-eager
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/BasileBron/Artificial-Intelligence-Lexical
https://github.com/Bayesian-Razor/papernotes
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://github.com/Belval/NRTR
https://github.com/Bhavnicksm/vanilla-transformer-jax
https://github.com/Bingo-W/Personalisation-in-LLM
https://github.com/BrianPulfer/PapersReimplementations
https://github.com/CERT-Lab/fed-sb
FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models
https://github.com/CGCL-codes/naturalcc
https://github.com/CKPOON0619/Kaggle2Sigma
https://github.com/CLUEbenchmark/CLUEPretrainedModels
CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model
https://github.com/CP8315-NNP-JLOPEZ/NNP-Chatbot
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/CSDN-AI7/image_caption
https://github.com/CVxTz/music_genre_classification
https://github.com/Caesarzhang/bert-zh
https://github.com/CambridgeIIS/Gesture-Keyboard-Traj-Gen
https://github.com/CatherineWong/dancin_seq2seq
https://github.com/Cathy-t/HELLO_image
https://github.com/Cenrax/Image-Captioning-with-Translation-
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/Chang-Chia-Chi/SaintPlus-Knowledge-Tracing-Pytorch
https://github.com/Charliesgithub20221030/BERT
https://github.com/ChenWu98/Point-Then-Operate
A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer
https://github.com/Chinnu1103/Machine-Translation-using-Transformers
https://github.com/Chloejay/image_caption_app
https://github.com/Chonwai/Learning_BERT
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.