README.md

---
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]'
        }
    ]
}