Attention-based Recurrent Convolutional Neural Network for
            Automatic Essay Scoring

Dong, Fangmin; Zhang, Yue; Yang, Jie

doi:10.18653/v1/k17-1017

Cited by 179 publications

(220 citation statements)

References 25 publications

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“…There are also more recent approaches for learning AES models that do not assume a set of predefined features. These approaches are based on deep architectures, and include (Alikaniotis et al, 2016;Taghipour and Ng, 2016;Riordan et al, 2017;Dong et al, 2017). Finally, there also models based on domain adaptation (Phandi et al, 2015) and unsupervised learning (Chen et al, 2010).…”

Section: Related Workmentioning

confidence: 99%

Automated Essay Scoring in the Presence of Biased Ratings

Amorim¹,

Cançado²,

Veloso³

2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

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Studies in Social Sciences have revealed that when people evaluate someone else, their evaluations often reflect their biases. As a result, rater bias may introduce highly subjective factors that make their evaluations inaccurate. This may affect automated essay scoring models in many ways, as these models are typically designed to model (potentially biased) essay raters. While there is sizeable literature on rater effects in general settings, it remains unknown how rater bias affects automated essay scoring. To this end, we present a new annotated corpus containing essays and their respective scores. Different from existing corpora, our corpus also contains comments provided by the raters in order to ground their scores. We present features to quantify rater bias based on their comments, and we found that rater bias plays an important role in automated essay scoring. We investigated the extent to which rater bias affects models based on hand-crafted features. Finally, we propose to rectify the training set by removing essays associated with potentially biased scores while learning the scoring model.

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Section: Related Workmentioning

confidence: 99%

Automated Essay Scoring in the Presence of Biased Ratings

Amorim¹,

Cançado²,

Veloso³

2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

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“…The combination of CNN and RNN has been adapted by several NLP tasks such as text summarization (Cheng and Lapata, 2016), essay scoring (Dong et al, 2017), sentiment analysis , or even reading comprehension (Dhingra et al, 2017). Unlike previous works that feed a sequence of sentences encoded by CNN to RNN, a sequence of utterances is encoded by CNN in our model, where each utterance is spoken by a distinct speaker and contains one or more sentences that are coherent in topics.…”

Section: Approachmentioning

confidence: 99%

Challenging Reading Comprehension on Daily Conversation: Passage Completion on Multiparty Dialog

Ma¹,

Jurczyk²,

Choi³

2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

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This paper presents a new corpus and a robust deep learning architecture for a task in reading comprehension, passage completion, on multiparty dialog. Given a dialog in text and a passage containing factual descriptions about the dialog where mentions of the characters are replaced by blanks, the task is to fill the blanks with the most appropriate character names that reflect the contexts in the dialog. Since there is no dataset that challenges the task of passage completion in this genre, we create a corpus by selecting transcripts from a TV show that comprise 1,681 dialogs, generating passages for each dialog through crowdsourcing, and annotating mentions of characters in both the dialog and the passages. Given this dataset, we build a deep neural model that integrates rich feature extraction from convolutional neural networks into sequence modeling in recurrent neural networks, optimized by utterance and dialog level attentions. Our model outperforms the previous state-of-the-art model on this task in a different genre using bidirectional LSTM, showing a 13.0+% improvement for longer dialogs. Our analysis shows the effectiveness of the attention mechanisms and suggests a direction to machine comprehension on multiparty dialog.

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“…The outputs of this layer are two matrices, L E ∈ R Se×We×d L for the essay and L A ∈ R Sa×Wa×d L for the article, where S e , S a , W e , W a , and d L are number of sentences of the essay and the article, length of sentences of the essay and the article, and the embedding size, respectively. Same to Dong et al (2017), a dropout is applied after the word embedding layer.…”

Section: Word Embedding Layermentioning

confidence: 99%

“…After the convolutional layer, a pooling layer is demanded to obtain the sentence representations. In this layer, we follow the same design presented by Dong et al (2017). The attention pooling is defined as equations below:…”

Section: Word Level Attention Pooling Layermentioning

confidence: 99%

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Co-Attention Based Neural Network for Source-Dependent Essay Scoring

Zhang¹,

Litman²

2018

Proceedings of the Thirteenth Workshop on Innovative Use of NLP For Building Educational Applications

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This paper presents an investigation of using a co-attention based neural network for source-dependent essay scoring. We use a coattention mechanism to help the model learn the importance of each part of the essay more accurately. Also, this paper shows that the coattention based neural network model provides reliable score prediction of source-dependent responses. We evaluate our model on two source-dependent response corpora. Results show that our model outperforms the baseline on both corpora. We also show that the attention of the model is similar to the expert opinions with examples.

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Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring

Cited by 179 publications

References 25 publications

Automated Essay Scoring in the Presence of Biased Ratings

Automated Essay Scoring in the Presence of Biased Ratings

Challenging Reading Comprehension on Daily Conversation: Passage Completion on Multiparty Dialog

Co-Attention Based Neural Network for Source-Dependent Essay Scoring

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