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DART: Open-Domain Structured Data Record to Text Generation

arXiv 2007.02871 ↗First posted 6 Jul 2020cs.CLcited by 144

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Abstract (arXiv)

We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-Text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures. To this end, we propose a procedure of extracting semantic triples from tables that encodes their structures by exploiting the semantic dependencies among table headers and the table title. Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and dialogue-act-based meaning representation tasks by utilizing techniques such as: tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing. We present systematic evaluation on DART as well as new state-of-the-art results on WebNLG 2017 to show that DART (1) poses new challenges to existing data-to-text datasets and (2) facilitates out-of-domain generalization. Our data and code can be found at https://github.com/Yale-LILY/dart.

Key facts. Cited by 144 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jul 2020.

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Citing papers per year: 2021: 24; 2022: 35; 2023: 31; 2024: 38; 2025: 13; 2026: 3.

144 papers.

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  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  3. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  4. 2021Large Language Models Can Be Strong Differentially Private Learnerscited by 405
  5. 2021Differentially Private Fine-tuning of Language Modelscited by 392
  6. 2022UL2: Unifying Language Learning Paradigmscited by 292
  7. 2021The GEM Benchmark: Natural Language Generation, its Evaluation and Metricscited by 239
  8. 2021SPoT: Better Frozen Model Adaptation through Soft Prompt Transfercited by 235
  9. 2022OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalizationcited by 235
  10. 2022UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Modelscited by 231
  11. 2022DyLoRA: Parameter Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptationcited by 229
  12. 2021FeTaQA: Free-form Table Question Answeringcited by 174
38 more
  1. 2021ExT5: Towards Extreme Multi-Task Scaling for Transfer Learningcited by 161
  2. 2023Unified Demonstration Retriever for In-Context Learningcited by 141
  3. 2022AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuningcited by 137
  4. 2022A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Modelscited by 128
  5. 2021HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generationcited by 117
  6. 2022Large Language Models are few(1)-shot Table Reasonerscited by 116
  7. 2023f-Divergence Minimization for Sequence-Level Knowledge Distillationcited by 112
  8. 2022State-of-the-art generalisation research in NLP: A taxonomy and reviewcited by 97
  9. 2022Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacycited by 97
  10. 2023Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsingcited by 93
  11. 2024Representation Noising: A Defence Mechanism Against Harmful Finetuningcited by 93
  12. 2023UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluationcited by 74
  13. 2024In-context Learning with Retrieved Demonstrations for Language Models: A Surveycited by 69
  14. 2023A Multi-Task Embedder For Retrieval Augmented LLMscited by 63
  15. 2023Learning to Retrieve In-Context Examples for Large Language Modelscited by 60
  16. 2021HTLM: Hyper-Text Pre-Training and Prompting of Language Modelscited by 58
  17. 2024Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Applicationcited by 55
  18. 2022Exploring the Limits of Differentially Private Deep Learning with Group-wise Clippingcited by 52
  19. 2024Large Language Model for Table Processing: A Surveycited by 52
  20. 2024Riemannian Preconditioned LoRA for Fine-Tuning Foundation Modelscited by 51
  21. 2023Kronecker-Factored Approximate Curvature for Modern Neural Network Architecturescited by 49
  22. 2021Structural Adapters in Pretrained Language Models for AMR-to-text Generationcited by 48
  23. 2023Adapting Large Language Models to Domains via Reading Comprehensioncited by 48
  24. 2021Text-to-Table: A New Way of Information Extractioncited by 47
  25. 2023Watermarking Conditional Text Generation for AI Detection: Unveiling Challenges and a Semantic-Aware Watermark Remedycited by 45
  26. 2024Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologiescited by 44
  27. 2025Parameter-Efficient Fine-Tuning for Foundation Modelscited by 43
  28. 2022IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languagescited by 42
  29. 2023When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and Limitationscited by 41
  30. 2022BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Banglacited by 40
  31. 2024SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Modelscited by 37
  32. 2022GEMv2: Multilingual NLG Benchmarking in a Single Line of Codecited by 36
  33. 2023Why Is Public Pretraining Necessary for Private Model Training?cited by 36
  34. 2022Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Modelscited by 34
  35. 2021Open Domain Question Answering with A Unified Knowledge Interfacecited by 32
  36. 2022Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learningcited by 32
  37. 2022Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsingcited by 30
  38. 2024AutoLoRA: Automatically Tuning Matrix Ranks in Low-Rank Adaptation Based on Meta Learningcited by 30

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