Abstract
AbstractThis paper presents a relation-centric algorithm for solving arithmetic word problems (AWPs) by synergizing a syntax-semantics extractor for extracting explicit relations, and a neural network miner for mining implicit relations. This is the first algorithm that has a specific component to acquire implicit knowledge items for solving AWPs. This paper proposes a three-phase scheme to decompose the challenging task of designing an algorithm for solving AWPs into three smaller tasks. The first phase proposes a state-action paradigm; the second phase instantiates the paradigm into a relation-centric approach; and the third phase implements a relation-centric algorithm for solving AWPs. There are two main steps in the proposed algorithm: problem understanding and symbolic solver. By adopting the relation-centric approach, problem understanding becomes a task of relation acquisition. For conducting the task of relation acquisition, a relaxed syntax-semantics method first extracts a group of explicit relation candidates. In parallel, a neural network miner acquires implicit relation candidates. The miner computes the vectors encoded by BERT to determine which implicit relations should be added. Thus, problem understanding can acquire both explicit relations and implicit relations, which addresses the challenge of building a problem understanding method that can acquire all the knowledge items to find the solution. In the subsequent step of symbolic solver, a fusion procedure forms a distilled set of relations from all the candidates by discarding unnecessary relations. Experimentation on nine benchmark datasets validates the superiority of the proposed algorithm that outperforms the state-of-the-art algorithms.
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
China Postdoctoral Science Foundation
ARC Discovery Project
Publisher
Springer Science and Business Media LLC
Subject
Computational Mathematics,Engineering (miscellaneous),Information Systems,Artificial Intelligence
Reference51 articles.
1. Zhang D, Wang L, Zhang L, Dai BT, Shen HT (2019) The gap of semantic parsing: A survey on automatic math word problem solvers. IEEE Trans Pattern Anal Mach Intell 42(9):2287–2305
2. Faldu K, Sheth A, Kikani P, Gaur M, Avasthi A (2021) Towards tractable mathematical reasoning: Challenges, strategies, and opportunities for solving math word problems. arXiv:2111.05364
3. Bekoulis G, Papagiannopoulou C, Deligiannis N (2023) A review on fact extraction and verification. ACM Comput Surv 55(1):1–35
4. Wang Y, Liu X, Shi S (2017) Deep neural solver for math word problems. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. pp. 845–854
5. Xie Z, Sun S (2019) A goal-driven tree-structured neural model for math word problems. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), pp. 5299–5305
Cited by
10 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献