Mapping Trigonometric Problems Using Deep Learning

• Neiva Larisane Kuyven UNIFTEC University Center (UNIFTEC) / Federal University of Rio Grande do Sul (UFRGS)
• Vinícius João de Barros Vanzin UNIFTEC University Center (UNIFTEC)
• Carlos André Antunes UNIFTEC University Center (UNIFTEC)
• Alexandra Cemin UNIFTEC University Center (UNIFTEC)
• João Luis Tavares da Silva UNIFTEC University Center (UNIFTEC)
• Liane Margarida Rockenbach Tarouco Federal University of Rio Grande do Sul (UFRGS)

DOI: https://doi.org/10.5753/cbie.sbie.2018.1513

Abstract

This paper presents a deep neural solver to automatically map trigonometric problems to equation models as part of a larger project of an Intelligent Tutor System in the area of Trigonometry. This approach directly translates mathematical problems into equation templates using a Recurrent Neural Network (RNN) model, combining a model based on linguistic knowledge to treat the trigonometric context. Experiments were conducted using a Chatbot for interaction with students.

Keywords: deep learning, trigonometric problems, equation models, recurrent neural network (RNN), intelligent tutoring system

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