no code implementations • • Cheng-Chung Fan, Chia-Chih Kuo, Shang-Bao Luo, Pei-Jun Liao, Kuang-Yu Chang, Chiao-Wei Hsu, Meng-Tse Wu, Shih-Hong Tsai, Tzu-Man Wu, Aleksandra Smolka, Chao-Chun Liang, Hsin-Min Wang, Kuan-Yu Chen, Yu Tsao, Keh-Yih Su
Only a few of them adopt several answer generation modules for providing different mechanisms; however, they either lack an aggregation mechanism to merge the answers from various modules, or are too complicated to be implemented with neural networks.
We construct two math datasets and show the effectiveness of our algorithms that they can retrieve the required knowledge for problem-solving.
We present ASDiv (Academia Sinica Diverse MWP Dataset), a diverse (in terms of both language patterns and problem types) English math word problem (MWP) corpus for evaluating the capability of various MWP solvers.
We present a novel approach to answer the Chinese elementary school Social Study Multiple Choice questions.
We introduce MeSys, a meaning-based approach, for solving English math word problems (MWPs) via understanding and reasoning in this paper.
This paper presents a meaning-based statistical math word problem (MWP) solver with understanding, reasoning and explanation.