|
--- |
|
language: en |
|
tags: |
|
- AMRBART |
|
license: mit |
|
--- |
|
|
|
## AMRBART (large-sized model) |
|
|
|
AMRBART model is continually pre-trained on the English text and AMR Graphs based on the BART model. It was introduced in the paper: [Graph Pre-training for AMR Parsing and Generation](https://arxiv.org/pdf/2203.07836.pdf) by bai et al. in ACL 2022 and first released in [this repository](https://github.com/muyeby/AMRBART). |
|
|
|
## Model description |
|
|
|
AMRBART follows the BART model which uses a transformer encoder-encoder architecture. AMRBART is pre-trained with 6 tasks: |
|
|
|
+ learning to reconstruct the text based on the corrupted text. |
|
+ learning to reconstruct AMR graphs based on the corrupted AMR graph. |
|
+ learning to reconstruct the text based on the corrupted text and its corresponding AMR graph. |
|
+ learning to reconstruct an AMR graph based on the corrupted AMR graph and its corresponding text. |
|
+ learning to reconstruct the text based on the corrupted text and its corresponding corrupted AMR graph. |
|
+ learning to reconstruct an AMR graph based on the corrupted AMR graph and its corresponding corrupted text. |
|
|
|
AMRBART is particularly effective when fine-tuned for AMR parsing and AMR-to-text generation tasks. |
|
|
|
## Training data |
|
|
|
The AMRBART model is pre-trained on [AMR3.0](https://catalog.ldc.upenn.edu/LDC2020T02), a dataset consisting of 55,635 |
|
training instances and [English Gigaword](https://catalog.ldc.upenn.edu/LDC2003T05) (we randomly sampled 200,000 sentences). |
|
|
|
## Intended uses & limitations |
|
|
|
You can use the raw model for either AMR encoding or AMR parsing, but it's mostly intended to |
|
be fine-tuned on a downstream task. |
|
|
|
## How to use |
|
Here is how to initialize this model in PyTorch: |
|
|
|
```python |
|
from transformers import BartForConditionalGeneration |
|
model = BartForConditionalGeneration.from_pretrained("xfbai/AMRBART-large") |
|
``` |
|
Please refer to [this repository](https://github.com/muyeby/AMRBART) for tokenizer initialization and data preprocessing. |
|
|
|
|
|
## BibTeX entry and citation info |
|
Please cite this paper if you find this model helpful |
|
|
|
```bibtex |
|
@inproceedings{bai-etal-2022-graph, |
|
title = "Graph Pre-training for {AMR} Parsing and Generation", |
|
author = "Bai, Xuefeng and |
|
Chen, Yulong and |
|
Zhang, Yue", |
|
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", |
|
month = may, |
|
year = "2022", |
|
address = "Online", |
|
publisher = "Association for Computational Linguistics", |
|
url = "todo", |
|
doi = "todo", |
|
pages = "todo" |
|
} |
|
``` |
