@inproceedings{naseem-etal-2021-semantics,
title = "A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering",
author = "Naseem, Tahira and
Ravishankar, Srinivas and
Mihindukulasooriya, Nandana and
Abdelaziz, Ibrahim and
Lee, Young-Suk and
Kapanipathi, Pavan and
Roukos, Salim and
Gliozzo, Alfio and
Gray, Alexander",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.34",
doi = "10.18653/v1/2021.acl-short.34",
pages = "256--262",
abstract = "Relation linking is a crucial component of Knowledge Base Question Answering systems. Existing systems use a wide variety of heuristics, or ensembles of multiple systems, heavily relying on the surface question text. However, the explicit semantic parse of the question is a rich source of relation information that is not taken advantage of. We propose a simple transformer-based neural model for relation linking that leverages the AMR semantic parse of a sentence. Our system significantly outperforms the state-of-the-art on 4 popular benchmark datasets. These are based on either DBpedia or Wikidata, demonstrating that our approach is effective across KGs.",
}
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<abstract>Relation linking is a crucial component of Knowledge Base Question Answering systems. Existing systems use a wide variety of heuristics, or ensembles of multiple systems, heavily relying on the surface question text. However, the explicit semantic parse of the question is a rich source of relation information that is not taken advantage of. We propose a simple transformer-based neural model for relation linking that leverages the AMR semantic parse of a sentence. Our system significantly outperforms the state-of-the-art on 4 popular benchmark datasets. These are based on either DBpedia or Wikidata, demonstrating that our approach is effective across KGs.</abstract>
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%0 Conference Proceedings
%T A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering
%A Naseem, Tahira
%A Ravishankar, Srinivas
%A Mihindukulasooriya, Nandana
%A Abdelaziz, Ibrahim
%A Lee, Young-Suk
%A Kapanipathi, Pavan
%A Roukos, Salim
%A Gliozzo, Alfio
%A Gray, Alexander
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F naseem-etal-2021-semantics
%X Relation linking is a crucial component of Knowledge Base Question Answering systems. Existing systems use a wide variety of heuristics, or ensembles of multiple systems, heavily relying on the surface question text. However, the explicit semantic parse of the question is a rich source of relation information that is not taken advantage of. We propose a simple transformer-based neural model for relation linking that leverages the AMR semantic parse of a sentence. Our system significantly outperforms the state-of-the-art on 4 popular benchmark datasets. These are based on either DBpedia or Wikidata, demonstrating that our approach is effective across KGs.
%R 10.18653/v1/2021.acl-short.34
%U https://aclanthology.org/2021.acl-short.34
%U https://doi.org/10.18653/v1/2021.acl-short.34
%P 256-262
Markdown (Informal)
[A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering](https://aclanthology.org/2021.acl-short.34) (Naseem et al., ACL-IJCNLP 2021)
ACL
- Tahira Naseem, Srinivas Ravishankar, Nandana Mihindukulasooriya, Ibrahim Abdelaziz, Young-Suk Lee, Pavan Kapanipathi, Salim Roukos, Alfio Gliozzo, and Alexander Gray. 2021. A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 256–262, Online. Association for Computational Linguistics.