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 · 2021
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.Our code and datasets are available online 1 .