@inproceedings{ravishankar-etal-2022-two,
title = "A Two-Stage Approach towards Generalization in Knowledge Base Question Answering",
author = "Ravishankar, Srinivas and
Thai, Dung and
Abdelaziz, Ibrahim and
Mihindukulasooriya, Nandana and
Naseem, Tahira and
Kapanipathi, Pavan and
Rossiello, Gaetano and
Fokoue, Achille",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.408",
doi = "10.18653/v1/2022.findings-emnlp.408",
pages = "5571--5580",
abstract = "Most existing approaches for Knowledge Base Question Answering (KBQA) focus on a specific underlying knowledge base either because of inherent assumptions in the approach, or because evaluating it on a different knowledge base requires non-trivial changes. However, many popular knowledge bases share similarities in their underlying schemas that can be leveraged to facilitate generalization across knowledge bases. To achieve this generalization, we introduce a KBQA framework based on a 2-stage architecture that explicitly separates semantic parsing from the knowledge base interaction, facilitating transfer learning across datasets and knowledge graphs. We show that pretraining on datasets with a different underlying knowledge base can nevertheless provide significant performance gains and reduce sample complexity. Our approach achieves comparable or state-of-the-art performance for LC-QuAD (DBpedia), WebQSP (Freebase), SimpleQuestions (Wikidata) and MetaQA (Wikimovies-KG).",
}
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<abstract>Most existing approaches for Knowledge Base Question Answering (KBQA) focus on a specific underlying knowledge base either because of inherent assumptions in the approach, or because evaluating it on a different knowledge base requires non-trivial changes. However, many popular knowledge bases share similarities in their underlying schemas that can be leveraged to facilitate generalization across knowledge bases. To achieve this generalization, we introduce a KBQA framework based on a 2-stage architecture that explicitly separates semantic parsing from the knowledge base interaction, facilitating transfer learning across datasets and knowledge graphs. We show that pretraining on datasets with a different underlying knowledge base can nevertheless provide significant performance gains and reduce sample complexity. Our approach achieves comparable or state-of-the-art performance for LC-QuAD (DBpedia), WebQSP (Freebase), SimpleQuestions (Wikidata) and MetaQA (Wikimovies-KG).</abstract>
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%0 Conference Proceedings
%T A Two-Stage Approach towards Generalization in Knowledge Base Question Answering
%A Ravishankar, Srinivas
%A Thai, Dung
%A Abdelaziz, Ibrahim
%A Mihindukulasooriya, Nandana
%A Naseem, Tahira
%A Kapanipathi, Pavan
%A Rossiello, Gaetano
%A Fokoue, Achille
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F ravishankar-etal-2022-two
%X Most existing approaches for Knowledge Base Question Answering (KBQA) focus on a specific underlying knowledge base either because of inherent assumptions in the approach, or because evaluating it on a different knowledge base requires non-trivial changes. However, many popular knowledge bases share similarities in their underlying schemas that can be leveraged to facilitate generalization across knowledge bases. To achieve this generalization, we introduce a KBQA framework based on a 2-stage architecture that explicitly separates semantic parsing from the knowledge base interaction, facilitating transfer learning across datasets and knowledge graphs. We show that pretraining on datasets with a different underlying knowledge base can nevertheless provide significant performance gains and reduce sample complexity. Our approach achieves comparable or state-of-the-art performance for LC-QuAD (DBpedia), WebQSP (Freebase), SimpleQuestions (Wikidata) and MetaQA (Wikimovies-KG).
%R 10.18653/v1/2022.findings-emnlp.408
%U https://aclanthology.org/2022.findings-emnlp.408
%U https://doi.org/10.18653/v1/2022.findings-emnlp.408
%P 5571-5580
Markdown (Informal)
[A Two-Stage Approach towards Generalization in Knowledge Base Question Answering](https://aclanthology.org/2022.findings-emnlp.408) (Ravishankar et al., Findings 2022)
ACL
- Srinivas Ravishankar, Dung Thai, Ibrahim Abdelaziz, Nandana Mihindukulasooriya, Tahira Naseem, Pavan Kapanipathi, Gaetano Rossiello, and Achille Fokoue. 2022. A Two-Stage Approach towards Generalization in Knowledge Base Question Answering. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5571–5580, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.