Using grammar-corrected essays as a second input improves cross-prompt trait scoring, with the largest gains on grammar-related traits like Conventions.
Neural Multi-task Learning in Automated Assessment
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abstract
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.
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Towards Prompt Generalization: Grammar-aware Cross-Prompt Automated Essay Scoring
Using grammar-corrected essays as a second input improves cross-prompt trait scoring, with the largest gains on grammar-related traits like Conventions.