H-AES: Towards Automated Essay Scoring for Hindi

TL;DR

This paper explores automated essay scoring for Hindi using classical and advanced NLP models, demonstrating comparable performance to English AES despite Hindi's low-resource status.

Contribution

It adapts and evaluates state-of-the-art AES methods for Hindi, a low-resource language, using translated data and real-world corpus analysis.

Findings

Models achieve performance comparable to English AES.

End-to-end models outperform classical feature-based methods.

Prompt-specific behaviors influence model performance.

Abstract

The use of Natural Language Processing (NLP) for Automated Essay Scoring (AES) has been well explored in the English language, with benchmark models exhibiting performance comparable to human scorers. However, AES in Hindi and other low-resource languages remains unexplored. In this study, we reproduce and compare state-of-the-art methods for AES in the Hindi domain. We employ classical feature-based Machine Learning (ML) and advanced end-to-end models, including LSTM Networks and Fine-Tuned Transformer Architecture, in our approach and derive results comparable to those in the English language domain. Hindi being a low-resource language, lacks a dedicated essay-scoring corpus. We train and evaluate our models using translated English essays and empirically measure their performance on our own small-scale, real-world Hindi corpus. We follow this up with an in-depth analysis discussing prompt-specific behavior of different language models implemented.