@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://preview.aclanthology.org/add-emnlp-2024-awards/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."
}