# Neural Multi-task Learning in Automated Assessment

arXiv: https://arxiv.org/abs/1801.06830 · HTML: https://mlsanity.com/paper/1801.06830/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2018.

## Built on by (most cited first, 6 of 6)

- [Are pre-trained text representations useful for multilingual and multi-dimensional language proficiency modeling?](https://mlsanity.com/paper/2102.12971/) (2021)
- [Deep Learning Architecture for Automatic Essay Scoring](https://mlsanity.com/paper/2206.08232/) (2022)
- [How to Evaluate Your Dialogue Models: A Review of Approaches](https://mlsanity.com/paper/2108.01369/) (2021)
- [An overview of artificial intelligence in computer-assisted language learning](https://mlsanity.com/paper/2505.02032/) (2025)
- [Towards Prompt Generalization: Grammar-aware Cross-Prompt Automated Essay Scoring](https://mlsanity.com/paper/2502.08450/) (2025)
- [Automated Essay Scoring Using Grammatical Variety and Errors with Multi-Task Learning and Item Response Theory](https://mlsanity.com/paper/2406.08817/) (2024)

## Abstract (arXiv)

Grammatical error detection and automated essay scoring are two tasks in the area of automated assessment. Traditionally these tasks have been treated independently with different machine learning models and features used for each task. In this paper, we develop a multi-task neural network model that jointly optimises for both tasks, and in particular we show that neural automated essay scoring can be significantly improved. We show that while the essay score provides little evidence to inform grammatical error detection, the essay score is highly influenced by error detection.