https://github.com/rossiyareich/inknhue
공식 코드inkn'hue: Enhancing Manga Colorization from Multiple Priors with Alignment Multi-Encoder VAE
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/rungjoo/TransMC-data2text
공식 코드Transforming Multi-Conditioned Generation from Meaning Representation
https://github.com/scoopmatic/finnish-hockey-news-generation-paper
공식 코드Template-free Data-to-Text Generation of Finnish Sports News
https://github.com/sebastianGehrmann/diverse_ensembling
공식 코드End-to-End Content and Plan Selection for Data-to-Text Generation
https://github.com/shailzajolly/fsdt
공식 코드Search and Learn: Improving Semantic Coverage for Data-to-Text Generation
https://github.com/shark-nlp/cont
공식 코드https://github.com/sixiaozheng/contextualstory
공식 코드ContextualStory: Consistent Visual Storytelling with Spatially-Enhanced and Storyline Context
https://github.com/swiseman/neighbor-splicing
공식 코드Data-to-text Generation by Splicing Together Nearest Neighbors
https://github.com/szxiangjn/any-shot-data2text
공식 코드ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models
https://github.com/tensorflow/tensor2tensor
공식 코드https://github.com/thu-coai/JointGT
공식 코드JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs
https://github.com/tingyaohsu/VIST-Edit
공식 코드https://github.com/tkim-snu/GLACNet
공식 코드GLAC Net: GLocal Attention Cascading Networks for Multi-image Cued Story Generation
https://github.com/tuetschek/e2e-cleaning
공식 코드Semantic Noise Matters for Neural Natural Language Generation
https://github.com/tushar117/xalign
공식 코드XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages
https://github.com/ufal/nlgi_eval
공식 코드Evaluating Semantic Accuracy of Data-to-Text Generation with Natural Language Inference
https://github.com/ukplab/structadapt
공식 코드Structural Adapters in Pretrained Language Models for AMR-to-text Generation
https://github.com/usydnlp/rovist
공식 코드https://github.com/vejvarm/aspiro
공식 코드ASPIRO: Any-shot Structured Parsing-error-Induced ReprOmpting for Consistent Data-to-Text Generation
https://github.com/videocrafter/animate-a-story
공식 코드Animate-A-Story: Storytelling with Retrieval-Augmented Video Generation
https://github.com/wanghm92/rw_fg
공식 코드Revisiting Challenges in Data-to-Text Generation with Fact Grounding
https://github.com/wenhuchen/HDSA-Dialog
공식 코드Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention
https://github.com/wenhuchen/KGPT
공식 코드KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation
https://github.com/worldhellow/hallucinations-c2t
공식 코드https://github.com/xydaytoy/eva
공식 코드Bridging the Gap between Different Vocabularies for LLM Ensemble
https://github.com/xzy-xzy/spor
공식 코드https://github.com/yale-lily/r2d2
공식 코드https://github.com/young1993/tlm
공식 코드https://github.com/yxuansu/plangen
공식 코드Plan-then-Generate: Controlled Data-to-Text Generation via Planning
https://github.com/zhaoyiran924/adv-in-context-learning
공식 코드https://github.com/zurichnlp/nmtscore
공식 코드NMTScore: A Multilingual Analysis of Translation-based Text Similarity Measures
https://github.com/zychen423/KE-VIST
공식 코드https://github.com/04RR/SOTA-Vision
https://github.com/1wy/bert
https://github.com/2023-MindSpore-1/ms-code-14/tree/main/Transformer
https://github.com/2023-MindSpore-4/Code8/tree/main/AttentionCluster
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.