While question answering over knowledge bases (KBQA) has shown progress in addressing factoid questions, KBQA with numerical reasoning remains relatively unexplored. In this paper, we focus on the complex numerical reasoning in KBQA and propose a new task, NR-KBQA, which necessitates the ability to perform both multi-hop reasoning and numerical reasoning. We design a logic form in Python format called PyQL to represent the reasoning process of numerical reasoning questions. To facilitate the development of NR-KBQA, we present a large dataset called MarkQA, which is automatically constructed from a small set of seeds. Each question in MarkQA is equipped with its corresponding SPARQL query, alongside the step-by-step reasoning process in the QDMR format and PyQL program. Experimental results of some state-of-the-art QA methods on the MarkQA show that complex numerical reasoning in KBQA faces great challenges.
@article{arxiv.2310.15517,
title = {MarkQA: A large scale KBQA dataset with numerical reasoning},
author = {Xiang Huang and Sitao Cheng and Yuheng Bao and Shanshan Huang and Yuzhong Qu},
journal= {arXiv preprint arXiv:2310.15517},
year = {2023}
}
Comments
EMNLP 2023 main conference. Code: https://github.com/cdhx/MarkQA Homepage: http://ws.nju.edu.cn/MarkQA