Datasets:
Tasks:
Text2Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
File size: 1,405 Bytes
a6716aa e3241ad a6716aa e3241ad |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: AMR 3.0 Parsed
size_categories:
- 10K<n<100K
source_datasets:
- ldc2020t02
task_categories:
- text2text-generation
---
# Dataset Card for AMR 3.0 Parsed
## Dataset Description
- **Repository:** [LDC2020T02](https://catalog.ldc.upenn.edu/LDC2020T02)
- **Paper:** [AMR Paper](https://aclanthology.org/W13-2322.pdf)
- **Point of Contact:** [Your Contact Info]
### Dataset Summary
This dataset contains parsed Abstract Meaning Representation (AMR) annotations from the LDC2020T02 release, formatted as instruction-following conversations. Each example consists of a sentence and its corresponding AMR graph representation.
### Supported Tasks and Leaderboards
- **Tasks:** Semantic parsing, specifically generating AMR graphs from English sentences
- **Leaderboards:** [AMR Parsing](https://paperswithcode.com/task/amr-parsing)
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
An example looks like:
```python
{
'conversations': [
{
'role': 'user',
'content': 'Generate an Abstract Meaning Representation (AMR) graph for the following sentence: [sentence]'
},
{
'role': 'assistant',
'content': '[AMR graph]'
}
]
} |