Solving Turkish math word problems by sequence-to-sequence encoder-decoder models

Abstract

Solving math word problems (MWP) is a challenging task due to the semantic gap between natural language texts and mathematical equations. The main purpose of the task is to take a written math problem as input and produce a proper equation as output for solving that problem. This paper describes a sequence-to-sequence (seq2seq) neural model for automatically solving Turkish MWPs based on their semantic meanings in the text. It comprises a bidirectional encoder to comprehend the semantics of the problem by encoding the input sequence and a decoder with attention to extract the equation by tracking the semantic meanings of the output symbols. We investigate the success of several embedding types, pretrained language models, and neural models. Our research is novel in the sense that there exist no studies in Turkish on this natural language processing task that utilizes pretrained language models and neural models. There is also no Turkish dataset that can be used to train the neural models for the MWP task. As the first large-scale Turkish MWP dataset, we translated the well-known English MWP datasets into Turkish using a machine translation system. Although Turkish is an agglutinative and grammatically challenging language, the proposed models achieve around 72% accuracy on the dataset compiled from three English datasets.