https://github.com/Minghong-Xu/Neural_Topical_Expansion_for_UPDS
A Neural Topical Expansion Framework for Unstructured Persona-oriented Dialogue Generation
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.
A Neural Topical Expansion Framework for Unstructured Persona-oriented Dialogue Generation
IT5: Text-to-text Pretraining for Italian Language Understanding and Generation
IT5: Text-to-text Pretraining for Italian Language Understanding and Generation
DSP: Dynamic Sequence Parallelism for Multi-Dimensional Transformers
Learning Transferable Visual Models From Natural Language Supervision
Neural Machine Translation by Jointly Learning to Align and Translate
ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation
Learning Transferable Visual Models From Natural Language Supervision
WebNovelBench: Placing LLM Novelists on the Web Novel Distribution
PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation
Learning Transferable Visual Models From Natural Language Supervision
PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.