https://bitbucket.org/ZimingLi/dg-irl-aaai2019
공식 코드Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning
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.
Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning
QuaSE: Accurate Text Style Transfer under Quantifiable Guidance
MoNoise: Modeling Noise Using a Modular Normalization System
CQASUMM: Building References for Community Question Answering Summarization Corpora
SH2: Self-Highlighted Hesitation Helps You Decode More Truthfully
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack
IndicBART: A Pre-trained Model for Indic Natural Language Generation
Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model
A Misleading Gallery of Fluid Motion by Generative Artificial Intelligence
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation
Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation
Vietnamese Poem Generation & The Prospect Of Cross-Language Poem-To-Poem Translation
VANiLLa : Verbalized Answers in Natural Language at Large Scale
Truth or Mirage? Towards End-to-End Factuality Evaluation with LLM-Oasis
IPAD: Inverse Prompt for AI Detection -- A Robust and Explainable LLM-Generated Text Detector
Parameter Efficient Fine-tuning via Explained Variance Adaptation
Enhancing AI Assisted Writing with One-Shot Implicit Negative Feedback
On Variational Learning of Controllable Representations for Text without Supervision
Diverse Keyphrase Generation with Neural Unlikelihood Training
CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model
Long Text Generation via Adversarial Training with Leaked Information
MolMetaLM: a Physicochemical Knowledge-Guided Molecular Meta Language Model
Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.