@inproceedings{chen-etal-2021-retrack,
title = "{R}e{T}ra{C}k: A Flexible and Efficient Framework for Knowledge Base Question Answering",
author = "Chen, Shuang and
Liu, Qian and
Yu, Zhiwei and
Lin, Chin-Yew and
Lou, Jian-Guang and
Jiang, Feng",
editor = "Ji, Heng and
Park, Jong C. and
Xia, Rui",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-demo.39",
doi = "10.18653/v1/2021.acl-demo.39",
pages = "325--336",
abstract = "We present Retriever-Transducer-Checker (ReTraCk), a neural semantic parsing framework for large scale knowledge base question answering (KBQA). ReTraCk is designed as a modular framework to maintain high flexibility. It includes a retriever to retrieve relevant KB items efficiently, a transducer to generate logical form with syntax correctness guarantees and a checker to improve transduction procedure. ReTraCk is ranked at top1 overall performance on the GrailQA leaderboard and obtains highly competitive performance on the typical WebQuestionsSP benchmark. Our system can interact with users timely, demonstrating the efficiency of the proposed framework.",
}
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<abstract>We present Retriever-Transducer-Checker (ReTraCk), a neural semantic parsing framework for large scale knowledge base question answering (KBQA). ReTraCk is designed as a modular framework to maintain high flexibility. It includes a retriever to retrieve relevant KB items efficiently, a transducer to generate logical form with syntax correctness guarantees and a checker to improve transduction procedure. ReTraCk is ranked at top1 overall performance on the GrailQA leaderboard and obtains highly competitive performance on the typical WebQuestionsSP benchmark. Our system can interact with users timely, demonstrating the efficiency of the proposed framework.</abstract>
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%0 Conference Proceedings
%T ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering
%A Chen, Shuang
%A Liu, Qian
%A Yu, Zhiwei
%A Lin, Chin-Yew
%A Lou, Jian-Guang
%A Jiang, Feng
%Y Ji, Heng
%Y Park, Jong C.
%Y Xia, Rui
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F chen-etal-2021-retrack
%X We present Retriever-Transducer-Checker (ReTraCk), a neural semantic parsing framework for large scale knowledge base question answering (KBQA). ReTraCk is designed as a modular framework to maintain high flexibility. It includes a retriever to retrieve relevant KB items efficiently, a transducer to generate logical form with syntax correctness guarantees and a checker to improve transduction procedure. ReTraCk is ranked at top1 overall performance on the GrailQA leaderboard and obtains highly competitive performance on the typical WebQuestionsSP benchmark. Our system can interact with users timely, demonstrating the efficiency of the proposed framework.
%R 10.18653/v1/2021.acl-demo.39
%U https://aclanthology.org/2021.acl-demo.39
%U https://doi.org/10.18653/v1/2021.acl-demo.39
%P 325-336
Markdown (Informal)
[ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering](https://aclanthology.org/2021.acl-demo.39) (Chen et al., ACL-IJCNLP 2021)
ACL
- Shuang Chen, Qian Liu, Zhiwei Yu, Chin-Yew Lin, Jian-Guang Lou, and Feng Jiang. 2021. ReTraCk: A Flexible and Efficient Framework 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: System Demonstrations, pages 325–336, Online. Association for Computational Linguistics.