Papers with Code 아카이브
Story Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 5
- 보존된 평가 기록
- 5
Story generation is the task of automatically generating a coherent narrative, often from a set of premises or a brief summary.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/GU-DataLab/Chatbot-Auditing-Framework
공식 코드It is Time to Develop an Auditing Framework to Promote Value Aware Chatbots
https://github.com/IIT-PAVIS/Positional_Diffusion
공식 코드Positional Diffusion: Ordering Unordered Sets with Diffusion Probabilistic Models
https://github.com/JianGuanTHU/StoryEndGen
공식 코드Story Ending Generation with Incremental Encoding and Commonsense Knowledge
https://github.com/JunjieHu/ReCo-RL
공식 코드What Makes A Good Story? Designing Composite Rewards for Visual Storytelling
https://github.com/MIMIFY/SS-Bench
공식 코드SS-GEN: A Social Story Generation Framework with Large Language Models
https://github.com/PlusLabNLP/Plot-guided-Coherence-Evaluation
공식 코드Plot-guided Adversarial Example Construction for Evaluating Open-domain Story Generation
https://github.com/PlusLabNLP/story-gen-BART
공식 코드Content Planning for Neural Story Generation with Aristotelian Rescoring
https://github.com/RUCAIBox/TextBox
공식 코드TextBox 2.0: A Text Generation Library with Pre-trained Language Models
https://github.com/Stry233/Visual-Story-Generation-Based-on-Emotional-and-Keyword-Scheme
공식 코드https://github.com/USArmyResearchLab/ARL-Creative-Visual-Storytelling
공식 코드Envisioning Narrative Intelligence: A Creative Visual Storytelling Anthology
https://github.com/YeLuoSuiYou/openstorypp
공식 코드Openstory++: A Large-scale Dataset and Benchmark for Instance-aware Open-domain Visual Storytelling
https://github.com/ZuyiZhou/Awesome-Interpretable-Cross-modal-Reasoning
공식 코드https://github.com/abisee/story-generation-eval
공식 코드Do Massively Pretrained Language Models Make Better Storytellers?
https://github.com/adbrei/renargen
공식 코드Returning to the Start: Generating Narratives with Related Endpoints
https://github.com/adlnlp/MultimodalLLM
공식 코드Multimodal Large Language Models and Tunings: Vision, Language, Sensors, Audio, and Beyond
https://github.com/aimagelab/alfie
공식 코드https://github.com/akskuchi/dhm-visual-storytelling
공식 코드https://github.com/akskuchi/groovist
공식 코드GROOViST: A Metric for Grounding Objects in Visual Storytelling
https://github.com/aplesner/flip-reasoning-challenge
공식 코드https://github.com/appleternity/story-plot-generation
공식 코드Conveying the Predicted Future to Users: A Case Study of Story Plot Prediction
https://github.com/arjunakula/Visual-Discourse-Parsing
공식 코드https://github.com/byliutao/1prompt1story
공식 코드One-Prompt-One-Story: Free-Lunch Consistent Text-to-Image Generation Using a Single Prompt
https://github.com/calclavia/story-generation
공식 코드Improving Neural Story Generation by Targeted Common Sense Grounding
https://github.com/cdjhz/discodvt
공식 코드DiscoDVT: Generating Long Text with Discourse-Aware Discrete Variational Transformer
https://github.com/cimeister/typical-sampling
공식 코드https://github.com/ckdjrkffz/controlling_keyword_position
공식 코드https://github.com/daniel3303/storyreasoning
공식 코드StoryReasoning Dataset: Using Chain-of-Thought for Scene Understanding and Grounded Story Generation
https://github.com/dapascual/DirectedBeamSearch
공식 코드Directed Beam Search: Plug-and-Play Lexically Constrained Language Generation
https://github.com/dapascual/k2t
공식 코드https://github.com/derekkk/mocha-emnlp22
공식 코드MOCHA: A Multi-Task Training Approach for Coherent Text Generation from Cognitive Perspective
https://github.com/dianaglzrico/neural-visual-storyteller
공식 코드https://github.com/donahowe/theatergen
공식 코드TheaterGen: Character Management with LLM for Consistent Multi-turn Image Generation
https://github.com/dong-river/corrpus
공식 코드CoRRPUS: Code-based Structured Prompting for Neurosymbolic Story Understanding
https://github.com/dwlmt/knowledgeable-stories
공식 코드https://github.com/edenbd/multimodalstory-demo
공식 코드FairyTailor: A Multimodal Generative Framework for Storytelling
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.