@InProceedings{dong-zhang-yang:2017:CoNLL,
  author    = {Dong, Fei  and  Zhang, Yue  and  Yang, Jie},
  title     = {Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring},
  booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {153--162},
  abstract  = {Neural network models have recently been applied to the task of automatic essay
	scoring, giving promising results. Existing work used recurrent neural networks
	and convolutional neural networks to model input essays, giving grades based on
	a single vector representation of the essay. On the other hand, the relative
	advantages of RNNs and CNNs have not been compared. In addition, different
	parts of the essay can contribute differently for scoring, which is not
	captured by existing models. We ad- dress these issues by building a
	hierarchical sentence-document model to represent essays, using the attention
	mechanism to automatically decide the relative weights of words and sentences.
	Results show that our model outperforms the previous state- of-the-art methods,
	demonstrating the effectiveness of the attention mechanism.},
  url       = {http://aclweb.org/anthology/K17-1017}
}