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Vanjape Rajas Mangesh – Dual Degree CSE

Vanjape Rajas Mangesh received his MS  Dual Degree in Computer Science and Engineering (CSE). His research work was supervised by Dr. Manish Shrivastava. Here’s a summary of  Vanjape Rajas Mangesh’s thesis Classification and Generation of Programming Word Problems.

 Modern Computers were first conceptualised by Charles Babbage in the 19th century. This first concept envisioned a machine in which a program (set of instructions to be performed by computer) were provided using punched cards. Over the past century, the idea of a programmable computer became a reality. As computers became more sophisticated and powerful, it became hard to program computers using punched cards. To solve this problem, programming languages were conceptualized and implemented. In last 50 years, computer programming has evolved from being a method of instructing computers into a necessary skill. Nowadays, It is even taught in primary schools.

Programming Word Problems (PWP) are used to teach Introductory Programming in schools and colleges. A PWP consists of a problem in natural language giving a background story and input/output format. The solver is expected to come up with a computer program which processes the input into expected output.

Programming Word Problems are similar to Mathematical Word Problems, albeit they are much harder to solve. A MathWord Problem (MWP) also consists of a story giving the preliminary conditions and a question at the end. The solver is expected to formulate the story as a mathematical equation, solve the equation and come up with a final answer for the question. In recent years, there has been growing interest in automatically solving MWPs in natural language. As PWPs are much harder to solve, there has not been much work in automatically solving them. As a preliminary step towards solving these problems, we introduce the problem of Programming Word Problem Classification. The problem is about predicting the algorithm class to which a PWP belongs. We create two datasets of a total of 4000 problems and pose this as a single and multi label classification task. We provide a deep learning based approach which gives an accuracy of 62.7 percent.

Along with the task of classifying Programming Word Problems, we introduce the problem of generation of Programming Word Problems. This problem is about automatically generating a Programming Word Problem. We propose a graph based representation for a PWP. Using the graph based representation and template based methods, we propose a method for generating programming word problems. We generate a set of Programming Word Problems and carry out human evaluation to evaluate correctness and relevance of the generated problems. The evaluations show that the generated problems are sufficiently realistic.