https://github.com/lipiji/hierarchical-encoder-decoder
A Hierarchical Neural Autoencoder for Paragraphs and Documents
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 Hierarchical Neural Autoencoder for Paragraphs and Documents
Long Text Generation via Adversarial Training with Leaked Information
Neural Machine Translation by Jointly Learning to Align and Translate
Multiple Generative Models Ensemble for Knowledge-Driven Proactive Human-Computer Dialogue Agent
SeqGenSQL -- A Robust Sequence Generation Model for Structured Query Language
PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking
Current Limitations of Language Models: What You Need is Retrieval
Neural Machine Translation by Jointly Learning to Align and Translate
Learning Transferable Visual Models From Natural Language Supervision
A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.