Solving Sinhala Language Arithmetic Problems using Neural Networks
This addresses a domain-specific problem for Sinhala language processing, but it is incremental as it builds on existing methods like ARIS and Mahoshadha.
The paper tackles solving arithmetic problems in Sinhala language by developing a neural network-based system that identifies keywords, questions, and operations, achieving an accuracy of 76% on the Mahoshadha2 dataset.
A methodology is presented to solve Arithmetic problems in Sinhala Language using a Neural Network. The system comprises of (a) keyword identification, (b) question identification, (c) mathematical operation identification and is combined using a neural network. Naive Bayes Classification is used in order to identify keywords and Conditional Random Field to identify the question and the operation which should be performed on the identified keywords to achieve the expected result. "One vs. all Classification" is done using a neural network for sentences. All functions are combined through the neural network which builds an equation to solve the problem. The paper compares each methodology in ARIS and Mahoshadha to the method presented in the paper. Mahoshadha2 learns to solve arithmetic problems with the accuracy of 76%.