Constrained Multi-Task Learning for Automated Essay Scoring
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Authors
Cummins, Ronan
Zhang, Meng
Briscoe, Edward
Publication Date
2016-08-12Journal Title
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics
Publisher
Association for Computational Linguistics
Language
English
Type
Conference Object
This Version
NA
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Cummins, R., Zhang, M., & Briscoe, E. (2016). Constrained Multi-Task Learning for Automated Essay Scoring. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics https://doi.org/10.18653/v1/P16-1075
Abstract
Supervised machine learning models for
automated essay scoring (AES) usually require
substantial task-specific training data
in order to make accurate predictions for
a particular writing task. This limitation
hinders their utility, and consequently
their deployment in real-world settings. In
this paper, we overcome this shortcoming
using a constrained multi-task pairwisepreference
learning approach that enables
the data from multiple tasks to be combined
effectively.
Furthermore, contrary to some recent research,
we show that high performance
AES systems can be built with little or no
task-specific training data. We perform a
detailed study of our approach on a publicly
available dataset in scenarios where
we have varying amounts of task-specific
training data and in scenarios where the
number of tasks increases.
Identifiers
External DOI: https://doi.org/10.18653/v1/P16-1075
This record's URL: https://www.repository.cam.ac.uk/handle/1810/260414
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