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        <title>TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring</title>
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            <namePart type="given">Yusuke</namePart>
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    <abstract>Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To close this gap, a two-stage deep neural network (TDNN) is proposed. In particular, in the first stage, using the rated essays for non-target prompts as the training data, a shallow model is learned to select essays with an extreme quality for the target prompt, serving as pseudo training data; in the second stage, an end-to-end hybrid deep model is proposed to learn a prompt-dependent rating model consuming the pseudo training data from the first step. Evaluation of the proposed TDNN on the standard ASAP dataset demonstrates a promising improvement for the prompt-independent AES task.</abstract>
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%0 Conference Proceedings
%T TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring
%A Jin, Cancan
%A He, Ben
%A Hui, Kai
%A Sun, Le
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F jin-etal-2018-tdnn
%X Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data. Despite their successes in prompt-dependent AES, how to effectively predict essay ratings under a prompt-independent setting remains a challenge, where the rated essays for the target prompt are not available. To close this gap, a two-stage deep neural network (TDNN) is proposed. In particular, in the first stage, using the rated essays for non-target prompts as the training data, a shallow model is learned to select essays with an extreme quality for the target prompt, serving as pseudo training data; in the second stage, an end-to-end hybrid deep model is proposed to learn a prompt-dependent rating model consuming the pseudo training data from the first step. Evaluation of the proposed TDNN on the standard ASAP dataset demonstrates a promising improvement for the prompt-independent AES task.
%R 10.18653/v1/P18-1100
%U https://aclanthology.org/P18-1100
%U https://doi.org/10.18653/v1/P18-1100
%P 1088-1097

Markdown (Informal)

[TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring](https://aclanthology.org/P18-1100) (Jin et al., ACL 2018)

• TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring (Jin et al., ACL 2018)

ACL

• Cancan Jin, Ben He, Kai Hui, and Le Sun. 2018. TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1088–1097, Melbourne, Australia. Association for Computational Linguistics.