https://github.com/anuragreddygv323/Important-stuff
Natural Language Processing: State of The Art, Current Trends and Challenges
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Natural Language Processing: State of The Art, Current Trends and Challenges
Learning Transferable Visual Models From Natural Language Supervision
compare-mt: A Tool for Holistic Comparison of Language Generation Systems
Learning Transferable Visual Models From Natural Language Supervision
Leveraging Graph to Improve Abstractive Multi-Document Summarization
ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation
A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Neural Machine Translation by Jointly Learning to Align and Translate
Neural Machine Translation by Jointly Learning to Align and Translate
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
Unsupervised Text Style Transfer using Language Models as Discriminators
Neural Machine Translation by Jointly Learning to Align and Translate
One Embedder, Any Task: Instruction-Finetuned Text Embeddings
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.