H-AES: Towards Automated Essay Scoring for Hindi
This addresses the problem of automated essay scoring for low-resource languages like Hindi, but it is incremental as it applies existing methods to new data.
The study tackled the lack of Automated Essay Scoring (AES) for Hindi by reproducing and comparing state-of-the-art methods, achieving results comparable to English benchmarks on a small-scale real-world Hindi corpus.
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.