Neural Multi-task Learning in Automated Assessment
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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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jan 2018.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 1; 2025: 2.
6 papers.
- 2021Are pre-trained text representations useful for multilingual and multi-dimensional language proficiency modeling?cited by 4
- 2022Deep Learning Architecture for Automatic Essay Scoringcited by 3
- 2021How to Evaluate Your Dialogue Models: A Review of Approachescited by 2
- 2025An overview of artificial intelligence in computer-assisted language learningcited by 2
- 2025Towards Prompt Generalization: Grammar-aware Cross-Prompt Automated Essay Scoringcited by 1
- 2024Automated Essay Scoring Using Grammatical Variety and Errors with Multi-Task Learning and Item Response Theorycited by 0
- 2025An overview of artificial intelligence in computer-assisted language learning4 May 2025
- 2025Towards Prompt Generalization: Grammar-aware Cross-Prompt Automated Essay Scoring12 Feb 2025
- 2024Automated Essay Scoring Using Grammatical Variety and Errors with Multi-Task Learning and Item Response Theory13 Jun 2024
- 2022Deep Learning Architecture for Automatic Essay Scoring16 Jun 2022
- 2021How to Evaluate Your Dialogue Models: A Review of Approaches3 Aug 2021
- 2021Are pre-trained text representations useful for multilingual and multi-dimensional language proficiency modeling?25 Feb 2021