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arXiv · 2112.06109

Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models

Yu Feng·Jing Zhang·Xiaokang Zhang·Lemao Liu·Cuiping Li·Hong Chen
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Abstract

Embedding-based methods are popular for Knowledge Base Question Answering (KBQA), but few current models have numerical reasoning skills and thus struggle to answer ordinal constrained questions. This paper proposes a new embedding-based KBQA framework which particularly takes numerical reasoning into account. We present NumericalTransformer on top of NSM, a state-of-the-art embedding-based KBQA model, to create NT-NSM. To enable better training, we propose two pre-training tasks with explicit numerical-oriented loss functions on two generated training datasets and a template-based data augmentation method for enriching ordinal constrained QA dataset. Extensive experiments on KBQA benchmarks demonstrate that with the help of our training algorithm, NT-NSM is empowered with numerical reasoning skills and substantially outperforms the baselines in answering ordinal constrained questions.

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Yu Feng, Jing Zhang, Xiaokang Zhang, Lemao Liu, Cuiping Li, Hong Chen. 2021-12-12. Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models. https://arxiv.org/abs/2112.06109

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