In problem solving, understanding the problem that one seeks to solve is an essential initial step. In this paper, we propose computational methods for facilitating problem understanding through the task of recognizing the unknown in specifications of long Math problems. We focus on the topic of Probability. Our experimental results show that learning models yield strong results on the task, a promising first step towards human interpretable, modular approaches to understanding long Math problems.
翻译:在解决问题时,理解人们寻求解决的问题是一个基本的第一步。在本文件中,我们提出通过承认长期数学问题规格未知的任务来帮助理解问题的计算方法。我们侧重于概率问题。我们的实验结果表明学习模式在这项任务上产生了巨大成果,这是朝着理解长期数学问题的人类可解释、模块化方法迈出的有希望的第一步。