An Augmented Benchmark Dataset for Geometric Question Answering through Dual Parallel Text Encoding

Jing Xiao

Abstract: Automatic math problem solving has attracted much attention of NLP researchers recently. However, most of the works focus on the solv- ing of Math Word Problems (MWPs). In this paper, we study on the Geometric Problem Solving based on neural networks. Solving ge- ometric problems requires the integration of text and diagram information as well as the knowledge of the relevant theorems. The lack of high-quality datasets and efficient neural geometric solvers impedes the development of automatic geometric problems solving. Based on GeoQA, we newly annotate 2,518 geo- metric problems with richer types and greater difficulty to form an augmented benchmark dataset GeoQA+1 , containing 6,027 problems in training set and 7,528 totally. We further perform data augmentation method to expand the training set to 12,054. Besides, we design a Dual Parallel text Encoder (DPE) to effi- ciently encode long and medium-length prob- lem text. The experimental results validate the effectiveness of GeoQA+ and DPE mod- ule, and the accuracy of automatic geometric problem solving is improved to 66.09%.