A Pretraining Numerical Reasoning Model for Ordinal Constrained Question Answering on Knowledge Base
Yu Feng, Jing Zhang, Gaole He, Wayne Xin Zhao, Lemao Liu, Quan Liu, Cuiping Li, Hong Chen
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.- Anthology ID:
- 2021.findings-emnlp.159
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1852–1861
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.159
- DOI:
- 10.18653/v1/2021.findings-emnlp.159
- Cite (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.
- Cite (Informal):
- A Pretraining Numerical Reasoning Model for Ordinal Constrained Question Answering on Knowledge Base (Feng et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.findings-emnlp.159.pdf