Augmenting Textual Qualitative Features in Deep Convolution Recurrent Neural Network for Automatic Essay Scoring

Tirthankar Dasgupta, Abir Naskar, Lipika Dey, Rupsa Saha


Abstract
In this paper we present a qualitatively enhanced deep convolution recurrent neural network for computing the quality of a text in an automatic essay scoring task. The novelty of the work lies in the fact that instead of considering only the word and sentence representation of a text, we try to augment the different complex linguistic, cognitive and psycological features associated within a text document along with a hierarchical convolution recurrent neural network framework. Our preliminary investigation shows that incorporation of such qualitative feature vectors along with standard word/sentence embeddings can give us better understanding about improving the overall evaluation of the input essays.
Anthology ID:
W18-3713
Volume:
Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
NLP-TEA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
93–102
Language:
URL:
https://aclanthology.org/W18-3713
DOI:
10.18653/v1/W18-3713
Bibkey:
Cite (ACL):
Tirthankar Dasgupta, Abir Naskar, Lipika Dey, and Rupsa Saha. 2018. Augmenting Textual Qualitative Features in Deep Convolution Recurrent Neural Network for Automatic Essay Scoring. In Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications, pages 93–102, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Augmenting Textual Qualitative Features in Deep Convolution Recurrent Neural Network for Automatic Essay Scoring (Dasgupta et al., NLP-TEA 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/W18-3713.pdf