https://github.com/hmrishavbandy/flipsketch
공식 코드FlipSketch: Flipping Static Drawings to Text-Guided Sketch Animations
Papers with Code 아카이브
Data-to-Text Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 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/hoangthangta/stsm
공식 코드https://github.com/hughplay/vtt
공식 코드https://github.com/iitmnlp/evaleval
공식 코드Perturbation CheckLists for Evaluating NLG Evaluation Metrics
https://github.com/iz2late/VIST-Character
공식 코드Detecting and Grounding Important Characters in Visual Stories
https://github.com/janenie/espn-nba-data
공식 코드Operations Guided Neural Networks for High Fidelity Data-To-Text Generation
https://github.com/jiaweisii/gorgeous
공식 코드Gorgeous: Create Your Desired Character Facial Makeup from Any Ideas
https://github.com/jordiclive/ControlPrefixes
공식 코드https://github.com/kakaobrain/stg
공식 코드Selective Token Generation for Few-shot Natural Language Generation
https://github.com/kasnerz/d2t_iterative_editing
공식 코드https://github.com/kasnerz/tabgenie
공식 코드https://github.com/kasnerz/zeroshot-d2t-pipeline
공식 코드https://github.com/kepei1106/cbst
공식 코드Curriculum-Based Self-Training Makes Better Few-Shot Learners for Data-to-Text Generation
https://github.com/langus0/critic-aware-decoding
공식 코드Critic-Driven Decoding for Mitigating Hallucinations in Data-to-text Generation
https://github.com/lemonqc/kg2text
공식 코드Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistency
https://github.com/littlekobe/AREL
공식 코드No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling
https://github.com/luka-group/lattice
공식 코드Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning
https://github.com/marco-roberti/char-data-to-text-gen
공식 코드Copy mechanism and tailored training for character-based data-to-text generation
https://github.com/mayer123/udt-qa
공식 코드Open Domain Question Answering with A Unified Knowledge Interface
https://github.com/menggehe/DRAW
공식 코드https://github.com/michelalorandi/d2t-gen-for-under-res-lang-w-llms
공식 코드https://github.com/mingdachen/WikiTableT
공식 코드WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections
https://github.com/mingdachen/syntactic-template-generation
공식 코드Leveraging Natural Supervision for Language Representation Learning and Generation
https://github.com/mingxiao-li/asymmetry-zigzag-storydiffusion
공식 코드https://github.com/mnschmit/unsupervised-graph-text-conversion
공식 코드An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing
https://github.com/muyeby/AMR-Backparsing
공식 코드https://github.com/ofa-sys/touchstone
공식 코드TouchStone: Evaluating Vision-Language Models by Language Models
https://github.com/passerby233/VSCMR-Visual-Storytelling-with-Corss-Modal-Rules
공식 코드https://github.com/pengbaolin/SC-GPT
공식 코드Few-shot Natural Language Generation for Task-Oriented Dialog
https://github.com/ratishsp/data2text-entity-py
공식 코드https://github.com/ratishsp/data2text-macro-plan-py
공식 코드https://github.com/ratishsp/data2text-plan-py
공식 코드https://github.com/ratishsp/data2text-seq-plan-py
공식 코드Data-to-text Generation with Variational Sequential Planning
https://github.com/recitalAI/QuestEval
공식 코드Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation
https://github.com/rikdz/GraphWriter
공식 코드Text Generation from Knowledge Graphs with Graph Transformers
https://github.com/rishidarkdevil/daam-i2i
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.