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
- Editors:
- Yuen-Hsien Tseng, Hsin-Hsi Chen, Vincent Ng, Mamoru Komachi
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/W18-3713.pdf