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--- |
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language: |
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- en |
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tags: |
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- NLG |
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- pytorch |
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- transformers |
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- BART |
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- Graph-to-Text |
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- Knowledge Graph |
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license: apache-2.0 |
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datasets: |
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- WebNLG |
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- EventNarrative |
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--- |
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# Model Description |
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We release our best performing models for the WebNLG and EventNarrative datasets from the paper GAP: *A Graph-aware Language Model Framework for |
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Knowledge Graph-to-Text Generation*. Our model is intended to be used on knowledge graphs in order to narrate their contents, giving a verbalization of the structured data. |
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# Paper |
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Please see our paper [here](https://arxiv.org/abs/2204.06674). |
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# Citation |
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If you found this model useful, please consider citing our paper: |
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``` |
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@inproceedings{colas-etal-2022-gap, |
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title = "{GAP}: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation", |
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author = "Colas, Anthony and |
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Alvandipour, Mehrdad and |
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Wang, Daisy Zhe", |
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booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", |
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month = oct, |
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year = "2022", |
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address = "Gyeongju, Republic of Korea", |
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publisher = "International Committee on Computational Linguistics", |
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url = "https://aclanthology.org/2022.coling-1.506", |
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pages = "5755--5769" |
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} |
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``` |
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# GitHub repo |
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Please see our GitHub [here](https://github.com/acolas1/GAP_COLING2022). |
