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

H-AES: Towards Automated Essay Scoring for Hindi