Tag Prediction of Competitive Programming Problems using Deep Learning Techniques

TL;DR

This paper explores using deep learning models like LSTM, GRU, and MLP to automatically classify and tag competitive programming problems by domain, aiming to assist programmers in finding suitable questions.

Contribution

It introduces a text classification approach for problem domain tagging in competitive programming using deep learning models on a real dataset from Codeforces.

Findings

MLP achieved 78.0% accuracy

Models effectively classify problem domains

Deep learning improves problem tagging

Abstract

In the past decade, the amount of research being done in the fields of machine learning and deep learning, predominantly in the area of natural language processing (NLP), has risen dramatically. A well-liked method for developing programming abilities like logic building and problem solving is competitive programming. It can be tough for novices and even veteran programmers to traverse the wide collection of questions due to the massive number of accessible questions and the variety of themes, levels of difficulty, and questions offered. In order to help programmers find questions that are appropriate for their knowledge and interests, there is a need for an automated method. This can be done using automated tagging of the questions using Text Classification. Text classification is one of the important tasks widely researched in the field of Natural Language Processing. In this paper, we present a way to use text classification techniques to determine the domain of a competitive programming problem. A variety of models, including are implemented LSTM, GRU, and MLP. The dataset has been scraped from Codeforces, a major competitive programming website. A total of 2400 problems were scraped and preprocessed, which we used as a dataset for our training and testing of models. The maximum accuracy reached using our model is 78.0% by MLP(Multi Layer Perceptron).