https://github.com/stevenlau6/bigsurvey
공식 코드Generating a Structured Summary of Numerous Academic Papers: Dataset and Method
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.
Generating a Structured Summary of Numerous Academic Papers: Dataset and Method
CADGE: Context-Aware Dialogue Generation Enhanced with Graph-Structured Knowledge Aggregation
Likelihood-based Mitigation of Evaluation Bias in Large Language Models
Out of the Cage: How Stochastic Parrots Win in Cyber Security Environments
An Extended Sequence Tagging Vocabulary for Grammatical Error Correction
Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange
CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation Models
BoK: Introducing Bag-of-Keywords Loss for Interpretable Dialogue Response Generation
DialoGen: Generalized Long-Range Context Representation for Dialogue Systems
Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk Decoding
ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning
Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning
Data-to-text Generation by Splicing Together Nearest Neighbors
Symphony Generation with Permutation Invariant Language Model
Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation
ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models
Evaluating Factuality in Generation with Dependency-level Entailment
Neural Syntactic Preordering for Controlled Paraphrase Generation
B2T Connection: Serving Stability and Performance in Deep Transformers
One-Shot Sensitivity-Aware Mixed Sparsity Pruning for Large Language Models
EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention
NGEP: A Graph-based Event Planning Framework for Story Generation
Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation
TwistList: Resources and Baselines for Tongue Twister Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.