
This model is used in the paper **Generative Relation Linking for Question Answering over Knowledge Bases**. [ArXiv](https://arxiv.org/abs/2108.07337), [GitHub](https://github.com/IBM/kbqa-relation-linking)

## Citation
```bibtex
@inproceedings{rossiello-genrl-2021,
 title={Generative relation linking for question answering over knowledge bases},
 author={Rossiello, Gaetano and Mihindukulasooriya, Nandana and Abdelaziz, Ibrahim and Bornea, Mihaela and Gliozzo, Alfio and Naseem, Tahira and Kapanipathi, Pavan},
 booktitle={International Semantic Web Conference},
 pages={321--337},
 year={2021},
 organization={Springer},
 url = "https://link.springer.com/chapter/10.1007/978-3-030-88361-4_19",
 doi = "10.1007/978-3-030-88361-4_19"
}
```

{"license": "apache-2.0"}
transformers pytorch bart text2text-generation arxiv:2108.07337 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


This model is used in the paper Generative Relation Linking for Question Answering over Knowledge Bases. ArXiv, GitHub


TAGS
#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #bart #text2text-generation #arxiv-2108.07337 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
