https://github.com/AmitMY/chimera
공식 코드Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation
Papers with Code 아카이브
Data-to-Text Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 26
- 보존된 평가 기록
- 122
A classic problem in natural-language generation (NLG) involves taking structured data, such as a table, as input, and producing text that adequately and fluently describes this data as output. Unlike machine translation, which aims for complete transduction of the sentence to be translated, this form of NLG is usually taken to require addressing (at least) two separate challenges: what to say, the selection of an appropriate subset of the input data to discuss, and how to say it, the surface realization of a generation.
<span style="color:grey; opacity: 0.6">( Image credit: Data-to-Text Generation with Content Selection and Planning )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/EagleW/Describing_a_Knowledge_Base
공식 코드https://github.com/EagleW/Stage-wise-Fine-tuning
공식 코드https://github.com/EdinburghNLP/wikigen
공식 코드https://github.com/FranxYao/Gumbel-CRF
공식 코드https://github.com/HMEIatJHU/SelectiveGeneration
공식 코드What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment
https://github.com/IBM/GPT-too-AMR2text
공식 코드GPT-too: A language-model-first approach for AMR-to-text generation
https://github.com/IIT-PAVIS/Positional_Diffusion
공식 코드Positional Diffusion: Ordering Unordered Sets with Diffusion Probabilistic Models
https://github.com/JasonObeid/Chart2Text
공식 코드Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model
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/KaijuML/PARENTing-rl
공식 코드https://github.com/KaijuML/data-to-text-hierarchical
공식 코드https://github.com/KaijuML/dtt-multi-branch
공식 코드Controlling Hallucinations at Word Level in Data-to-Text Generation
https://github.com/LiyingCheng95/EntityDescriptionGeneration
공식 코드ENT-DESC: Entity Description Generation by Exploring Knowledge Graph
https://github.com/RUCAIBox/Few-Shot-KG2Text
공식 코드Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models
https://github.com/RUCAIBox/TextBox
공식 코드TextBox 2.0: A Text Generation Library with Pre-trained Language Models
https://github.com/ReneeYe/VariationalTemplateMachine
공식 코드https://github.com/SUTDNLP/ZGen
공식 코드https://github.com/Soistesimmer/AMR-multiview
공식 코드Structural Information Preserving for Graph-to-Text Generation
https://github.com/ThiagoCF05/webnlg
공식 코드Neural data-to-text generation: A comparison between pipeline and end-to-end architectures
https://github.com/ThomasScialom/QuestEval
공식 코드Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation
https://github.com/UKPLab/SciGen
공식 코드Learning to Reason for Text Generation from Scientific Tables
https://github.com/UKPLab/emnlp2019-dualgraph
공식 코드Enhancing AMR-to-Text Generation with Dual Graph Representations
https://github.com/UKPLab/kg2text
공식 코드Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs
https://github.com/UKPLab/plms-graph2text
공식 코드Investigating Pretrained Language Models for Graph-to-Text Generation
https://github.com/USArmyResearchLab/ARL-Creative-Visual-Storytelling
공식 코드Envisioning Narrative Intelligence: A Creative Visual Storytelling Anthology
https://github.com/Yale-LILY/dart
공식 코드https://github.com/YeLuoSuiYou/openstorypp
공식 코드Openstory++: A Large-scale Dataset and Benchmark for Instance-aware Open-domain Visual Storytelling
https://github.com/ZhihongShao/Planning-based-Hierarchical-Variational-Model
공식 코드Long and Diverse Text Generation with Planning-based Hierarchical Variational Model
https://github.com/ZuyiZhou/Awesome-Interpretable-Cross-modal-Reasoning
공식 코드https://github.com/acolas1/EventNarrative
공식 코드EventNarrative: A large-scale Event-centric Dataset for Knowledge Graph-to-Text Generation
https://github.com/acolas1/GAP_COLING2022
공식 코드GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation
https://github.com/acolas1/kgsimple
공식 코드https://github.com/adlnlp/MultimodalLLM
공식 코드Multimodal Large Language Models and Tunings: Vision, Language, Sensors, Audio, and Beyond
https://github.com/agnesion/zero-shot-nlg-from-kgs-data
공식 코드Using Large Language Models for Zero-Shot Natural Language Generation from Knowledge Graphs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.