Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models

• URL: http://arxiv.org/abs/2112.06109v1
• Date: Sun, 12 Dec 2021 01:30:29 GMT
• Title: Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models
• Authors: Yu Feng, Jing Zhang, Xiaokang Zhang, Lemao Liu, Cuiping Li, Hong Chen
• Abstract summary: This paper proposes a new embedding-based KBQA framework which 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. Experiments on KBQA benchmarks demonstrate that NT-NSM is empowered with numerical reasoning skills and substantially outperforms the baselines in answering ordinal constrained questions.
• Score: 19.964729281684363
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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