@inproceedings{dasgupta-etal-2018-augmenting,
title = "Augmenting Textual Qualitative Features in Deep Convolution Recurrent Neural Network for Automatic Essay Scoring",
author = "Dasgupta, Tirthankar and
Naskar, Abir and
Dey, Lipika and
Saha, Rupsa",
editor = "Tseng, Yuen-Hsien and
Chen, Hsin-Hsi and
Ng, Vincent and
Komachi, Mamoru",
booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-3713",
doi = "10.18653/v1/W18-3713",
pages = "93--102",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Augmenting Textual Qualitative Features in Deep Convolution Recurrent Neural Network for Automatic Essay Scoring
%A Dasgupta, Tirthankar
%A Naskar, Abir
%A Dey, Lipika
%A Saha, Rupsa
%Y Tseng, Yuen-Hsien
%Y Chen, Hsin-Hsi
%Y Ng, Vincent
%Y Komachi, Mamoru
%S Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F dasgupta-etal-2018-augmenting
%X 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.
%R 10.18653/v1/W18-3713
%U https://aclanthology.org/W18-3713
%U https://doi.org/10.18653/v1/W18-3713
%P 93-102
Markdown (Informal)
[Augmenting Textual Qualitative Features in Deep Convolution Recurrent Neural Network for Automatic Essay Scoring](https://aclanthology.org/W18-3713) (Dasgupta et al., NLP-TEA 2018)
ACL