Papers with Code 아카이브
Text Generation
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- 하위 태스크
- 25
- 평가표
- 155
- 보존된 평가 기록
- 54
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/Shawn-Inspur/Yuan-1.0
Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot Learning
https://github.com/ShiYaya/Awesome_Evaluation_Metrics_for_Text_Generation
https://github.com/ShiminLei/LA-Dialog-Generation-System
Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation
https://github.com/Shinya-Kouda/kgc
https://github.com/ShivamRajSharma/Transformer-Architectures-From-Scratch
https://github.com/Shubham-SK/TreeOverAte
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/Shubham-SK/kronos
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/ShuvenduBikash/transformer_spelling_corrector
https://github.com/ShwetaBaranwal/Transformer
https://github.com/SimonDele/Glossary
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/Skumarr53/Attention-is-All-you-Need-PyTorch
https://github.com/SpikeKing/My-Bert
https://github.com/StatNLP/ada4asr
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/StevenLai1994/AM_and_LM
https://github.com/StillKeepTry/Transformer-PyTorch
https://github.com/StoneGH/bert
https://github.com/SubhamIO/Emotion-Recognition-in-Conversation
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
https://github.com/SunJiamei/LRP-imagecaptioning-pytorch
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/SwordYork/DCNMT
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/T9-LIN/MSc-Project
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/THUNLP-MT/THUMT
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/TSLNIHAOGIT/bert
https://github.com/TSLNIHAOGIT/bert_run
https://github.com/TYTYTYTYTYTYTYTYTY/558-project
https://github.com/TalkToTheGAN/REGAN
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://github.com/TamSiuhin/Per-Pcs
https://github.com/TaoQin/taoqin.github.io
BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining
https://github.com/TellinaTool/nl2bash
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/TengHu/Generating-Sequences-With-Recurrent-Neural-Networks
https://github.com/The-AI-Summer/self_attention
https://github.com/TheTensorDude/transformer-tf
https://github.com/ThilinaRajapakse/simpletransformers
Personalizing Dialogue Agents: I have a dog, do you have pets too?
https://github.com/TieDanCuihua/transformer-Attention-is-All-You-Need
https://github.com/TilakD/Image-Captioning
https://github.com/TobeyYang/S2S_Temp
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.