# H-AES: Towards Automated Essay Scoring for Hindi

arXiv: https://arxiv.org/abs/2302.14635 · HTML: https://mlsanity.com/paper/2302.14635/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 64 references that are in the map.
- First posted on arXiv 28 Feb 2023.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [MuRIL: Multilingual Representations for Indian Languages](https://mlsanity.com/paper/2103.10730/) (2021)
- [Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages](https://mlsanity.com/paper/2104.05596/) (2021)
- [Automatic Text Scoring Using Neural Networks](https://mlsanity.com/paper/1606.04289/) (2016)
- [On the Use of BERT for Automated Essay Scoring: Joint Learning of Multi-Scale Essay Representation](https://mlsanity.com/paper/2205.03835/) (2022)
- [Language models and Automated Essay Scoring](https://mlsanity.com/paper/1909.09482/) (2019)
- [Automated essay scoring with string kernels and word embeddings](https://mlsanity.com/paper/1804.07954/) (2018)
- [iNLTK: Natural Language Toolkit for Indic Languages](https://mlsanity.com/paper/2009.12534/) (2020)
- [SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring](https://mlsanity.com/paper/1711.04981/) (2017)
- [Get It Scored Using AutoSAS -- An Automated System for Scoring Short Answers](https://mlsanity.com/paper/2012.11243/) (2020)
- [Automated Essay Scoring Using Transformer Models](https://mlsanity.com/paper/2110.06874/) (2021)
- [Automated assessment of non-native learner essays: Investigating the role of linguistic features](https://mlsanity.com/paper/1612.00729/) (2016)
- [Automated Speech Scoring System Under The Lens: Evaluating and interpreting the linguistic cues for language proficiency](https://mlsanity.com/paper/2111.15156/) (2021)
- [Using Sampling to Estimate and Improve Performance of Automated Scoring Systems with Guarantees](https://mlsanity.com/paper/2111.08906/) (2021)
- [Taxonomic survey of Hindi Language NLP systems](https://mlsanity.com/paper/2102.00214/) (2021)
- [Vy=akarana: A Colorless Green Benchmark for Syntactic Evaluation in Indic Languages](https://mlsanity.com/paper/2103.00854/) (2021)

## Built on by (most cited first, 3 of 3)

- [A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science](https://mlsanity.com/paper/2403.14565/) (2024)
- [CoTAL: Human-in-the-Loop Prompt Engineering for Generalizable Formative Assessment Scoring and Feedback](https://mlsanity.com/paper/2504.02323/) (2025)
- [SARA: Unlocking Multilingual Knowledge in Mixture-of-Experts via Semantically Anchored Routing Alignment](https://mlsanity.com/paper/2606.25821/) (2026)

## Abstract (arXiv)

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.