DART is a large dataset for open-domain structured data record to text generation. DART consists of 82,191 examples across different domains with each input being a semantic RDF triple set derived from data records in tables and the tree ontology of the schema, annotated with sentence descriptions that cover all facts in the triple set.
Source: DART: Open-Domain Structured Data Record to Text Generation
Variants: DART
This dataset is used in 3 benchmarks:
Task | Model | Paper | Date |
---|---|---|---|
Data-to-Text Generation | self-mem + new data | Self-training from Self-memory in Data-to-text … | 2024-01-19 |
Text Generation | Control Prefixes (T5-large) | Control Prefixes for Parameter-Efficient Text … | 2021-10-15 |
Table-to-Text Generation | HTLM (fine-tuning) | HTLM: Hyper-Text Pre-Training and Prompting … | 2021-07-14 |
Table-to-Text Generation | GPT-2-Large (fine-tuning) | HTLM: Hyper-Text Pre-Training and Prompting … | 2021-07-14 |
Text Generation | T5 | The GEM Benchmark: Natural Language … | 2021-02-02 |
Text Generation | BART | The GEM Benchmark: Natural Language … | 2021-02-02 |
Recent papers with results on this dataset: