Fetching the paper…
Reading the bibliography…
In this paper, we present a new comparative study on automatic essay scoring (AES).
J. Cohen, “A coefficient of agreement for nominal scales,” Educational and psychological measurement , vol. 20, no. 1, pp. 37–46, 1960
1960
Earlier work this paper cites.
E. B. Page, “The imminence of… grading essays by computer,” The Phi Delta Kappan , vol. 47, no. 5, pp. 238–243, 1966
1966
Earlier work this paper cites.
E. B. Page, “Grading essays by computer: progress report.” in Proceedings of the Invitational Conference on Testing Problems , 1967, pp. 87–100
1967
Earlier work this paper cites.
——, “The use of the computer in analyzing student essays.” in International Review of Education , 1968, pp. 210–225
1968
Earlier work this paper cites.
M. T. Hagan and M. B. Menhaj, “Training feedforward networks with the marquardt algorithm,” IEEE transactions on Neural Networks , vol. 5, no. 6, pp. 989–993, 1994
1994
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
J. Arter, “Rubrics, scoring guides, and performance criteria: Classroom tools for assessing and improving student learning.” 2000
2000
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
D. M. Williamson, X. Xi, and F. J. Breyer, “A framework for evaluation and use of automated scoring,” Educational measurement: issues and practice , vol. 31, no. 1, pp. 2–13, 2012
2012
Earlier work this paper cites.
M. Schuster and K. Nakajima, “Japanese and korean voice search,” in 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2012, pp. 5149–5152
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in Advances in neural information processing systems , 2013, pp. 3111–3119
2013
Earlier work this paper cites.
R. Pascanu, T. Mikolov, and Y. Bengio, “On the difficulty of training recurrent neural networks,” in International conference on machine learning , 2013, pp. 1310–1318
2013
Cited alongside, same era.
K. L. Gwet, Handbook of inter-rater reliability: The definitive guide to measuring the extent of agreement among raters . Advanced Analytics, LLC, 2014
2014
Cited alongside, same era.
2014
Cited alongside, same era.
Y. B. Jason Yosinski, Jeff Clune and H. Lipson, “How transferable are features in deep neural networks?” Advances in neural information processing systems , pp. 3320–3328, 2014
2014
Cited alongside, same era.
A. H. Jafari and M. T. Hagan, “Application of new training methods for neural model reference control,” Engineering Applications of Artificial Intelligence , vol. 74, pp. 312 – 321, 2018. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0952197618301490
2018
Later among the works it cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv , no. 1810.04805, Oct 2018
2018
Later among the works it cites.
M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, “Deep contextualized word representations,” in Proc. of NAACL , 2018
2018
Later among the works it cites.
Howard and Ruder, “Universal language model fine-tuning for text classification,” arXiv , no. 1801.06146, 2018
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
K. Sakaguchi, M. Heilman, and N. Madnani, “Effective feature integration for automated short answer scoring,” in Proceedings of the 2015 conference of the North American Chapter of the association for computational linguistics: Human language technologies , 2015, pp. 1049–1054
2015
Cited alongside, same era.
M. D. Shermis, “Contrasting state-of-the-art in the machine scoring of short-form constructed responses,” Educational Assessment , vol. 20, no. 1, pp. 46–65, 2015. [Online]. Available: https://doi.org/10.1080/10627197.2015.997617
2015
Cited alongside, same era.
H. Yannakoudakis and R. Cummins, “Evaluating the performance of automated text scoring systems,” in Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications , 2015, pp. 213–223
2015
Cited alongside, same era.
A. H. Jafari and M. T. Hagan, “Enhanced recurrent network training,” in 2015 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2015, pp. 1–8
2015
Cited alongside, same era.
2016
Cited alongside, same era.
K. Taghipour and H. T. Ng, “A neural approach to automated essay scoring,” Conference on Empirical Methods in Natural Language Processing , no. 1905.05583, p. 1882–1891, November 2016
2016
Cited alongside, same era.
A. Vaswani, N. Shazee, N. Parmar, J. Uszkorei, L. Jone, A. N. Gomez, Łukasz Kaiser, and I. Polosukhin, “Attention is all you need,” arXiv , no. 1706.03762, Jun 2017
2017
Cited alongside, same era.
M. T. Hagan, H. B. Demuth, M. H. Beale, and O. D. Jesús, Neural Network Design, 2nd Edition . PWS Publishing, Boston
Cited in the paper.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, “Language models are unsupervised multitask learners,” OpenAI Blog , vol. 1, no. 8, 2019
2019
Closest in time.
Z. Yang, Z. Dai, Y. Yang, J. Carbonel, R. Salakhutdinov, and Q. V. Le, “Xlnet: Generalized autoregressive pretraining for language understanding,” arXiv , no. 1906.08237, Jun 2019
2019
Closest in time.
Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, “Roberta: A robustly optimized bert pretraining approach,” arXiv , no. 1907.11692, Jul 2019
2019
Closest in time.
Z. Dai, Z. Yang, Y. Yang, W. W. Cohen, J. Carbonell, Q. V. Le, and R. Salakhutdinov, “Transformer-xl: Attentive language models beyond a fixed-length context,” arXiv , no. 1901.02860, Jan 2019
2019
Closest in time.
O. L. C. D. M. Kevin Clark, Urvashi Khandelwal, “What does bert look at? an analysis of bert’s attention,” arXiv , no. 1906.0434, Jun 2019
2019
Closest in time.
2019
Closest in time.
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Gated feedback recurrent neural networks,” in International Conference on Machine Learning , 2015, pp. 2067–2075
2075
Closest in time.