@inproceedings{feng-etal-2021-pretraining-numerical,
title = "A Pretraining Numerical Reasoning Model for Ordinal Constrained Question Answering on Knowledge Base",
author = "Feng, Yu and
Zhang, Jing and
He, Gaole and
Zhao, Wayne Xin and
Liu, Lemao and
Liu, Quan and
Li, Cuiping and
Chen, Hong",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.159/",
doi = "10.18653/v1/2021.findings-emnlp.159",
pages = "1852--1861",
abstract = "Knowledge Base Question Answering (KBQA) is to answer natural language questions posed over knowledge bases (KBs). This paper targets at empowering the IR-based KBQA models with the ability of numerical reasoning for answering ordinal constrained questions. A major challenge is the lack of explicit annotations about numerical properties. To address this challenge, we propose a pretraining numerical reasoning model consisting of NumGNN and NumTransformer, guided by explicit self-supervision signals. The two modules are pretrained to encode the magnitude and ordinal properties of numbers respectively and can serve as model-agnostic plugins for any IR-based KBQA model to enhance its numerical reasoning ability. Extensive experiments on two KBQA benchmarks verify the effectiveness of our method to enhance the numerical reasoning ability for IR-based KBQA models."
}
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<abstract>Knowledge Base Question Answering (KBQA) is to answer natural language questions posed over knowledge bases (KBs). This paper targets at empowering the IR-based KBQA models with the ability of numerical reasoning for answering ordinal constrained questions. A major challenge is the lack of explicit annotations about numerical properties. To address this challenge, we propose a pretraining numerical reasoning model consisting of NumGNN and NumTransformer, guided by explicit self-supervision signals. The two modules are pretrained to encode the magnitude and ordinal properties of numbers respectively and can serve as model-agnostic plugins for any IR-based KBQA model to enhance its numerical reasoning ability. Extensive experiments on two KBQA benchmarks verify the effectiveness of our method to enhance the numerical reasoning ability for IR-based KBQA models.</abstract>
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%0 Conference Proceedings
%T A Pretraining Numerical Reasoning Model for Ordinal Constrained Question Answering on Knowledge Base
%A Feng, Yu
%A Zhang, Jing
%A He, Gaole
%A Zhao, Wayne Xin
%A Liu, Lemao
%A Liu, Quan
%A Li, Cuiping
%A Chen, Hong
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F feng-etal-2021-pretraining-numerical
%X Knowledge Base Question Answering (KBQA) is to answer natural language questions posed over knowledge bases (KBs). This paper targets at empowering the IR-based KBQA models with the ability of numerical reasoning for answering ordinal constrained questions. A major challenge is the lack of explicit annotations about numerical properties. To address this challenge, we propose a pretraining numerical reasoning model consisting of NumGNN and NumTransformer, guided by explicit self-supervision signals. The two modules are pretrained to encode the magnitude and ordinal properties of numbers respectively and can serve as model-agnostic plugins for any IR-based KBQA model to enhance its numerical reasoning ability. Extensive experiments on two KBQA benchmarks verify the effectiveness of our method to enhance the numerical reasoning ability for IR-based KBQA models.
%R 10.18653/v1/2021.findings-emnlp.159
%U https://aclanthology.org/2021.findings-emnlp.159/
%U https://doi.org/10.18653/v1/2021.findings-emnlp.159
%P 1852-1861
Markdown (Informal)
[A Pretraining Numerical Reasoning Model for Ordinal Constrained Question Answering on Knowledge Base](https://aclanthology.org/2021.findings-emnlp.159/) (Feng et al., Findings 2021)
ACL
- Yu Feng, Jing Zhang, Gaole He, Wayne Xin Zhao, Lemao Liu, Quan Liu, Cuiping Li, and Hong Chen. 2021. A Pretraining Numerical Reasoning Model for Ordinal Constrained Question Answering on Knowledge Base. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1852–1861, Punta Cana, Dominican Republic. Association for Computational Linguistics.