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