Text Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/xiaopp123/bert_explain
https://github.com/xieyxclack/factual_coco
https://github.com/xinding-sys/StreamMind
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
https://github.com/xingjian-zhang/massw
https://github.com/xingniu/sockeye
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/xitianxiaofeixue/BERT
https://github.com/xlang-ai/instructor-embedding
One Embedder, Any Task: Instruction-Finetuned Text Embeddings
https://github.com/xmu-xiaoma666/External-Attention-pytorch
https://github.com/xmubq/dtr-text
https://github.com/xnancy/convai-transformer
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
https://github.com/xuehaouwa/lm-load-forecasting
PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting
https://github.com/xuewyang/Fashion_Captioning
https://github.com/xuuhuang/lost_in_the_src
INSTRUCTSCORE: Explainable Text Generation Evaluation with Finegrained Feedback
https://github.com/xuzhezhaozhao/bert_reading
https://github.com/xydaytoy/BMI-NMT
https://github.com/yahah100/text_summarization
Pretraining-Based Natural Language Generation for Text Summarization
https://github.com/yakazimir/Code-Datasets
The Code2Text Challenge: Text Generation in Source Code Libraries
https://github.com/yale-lily/ehrkit-2022
Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation
https://github.com/yangbang18/clfm
https://github.com/yangkai2g7k/nl2code
A Syntactic Neural Model for General-Purpose Code Generation
https://github.com/yangkevin2/emnlp22-re3-story-generation
https://github.com/yangletliu/finlora
https://github.com/yangrui525/kg-rank
Integrating UMLS Knowledge into Large Language Models for Medical Question Answering
https://github.com/yangyi-chen/maya
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack
https://github.com/yangyucheng000/University/tree/main/model-1/plbart
Unified Pre-training for Program Understanding and Generation
https://github.com/yangyucheng000/University/tree/main/model-2/blip_2
https://github.com/yangyucheng000/University/tree/main/model-3/rag
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/yanqi1811/attention-is-all-you-need
https://github.com/yashbonde/RNN-sim
https://github.com/yashk2810/Image-Captioning
https://github.com/yaushian/Unparalleled-Text-Summarization-using-GAN
https://github.com/yeeeqichen/Bert
https://github.com/yejy53/ep-bev
Where am I? Cross-View Geo-localization with Natural Language Descriptions
https://github.com/yhpeter/awesome-rag-evaluation
https://github.com/yinghao1019/NLP_and_DL_practice
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.