---
title: "Automatic Text Scoring Using Neural — Alikaniotis 2016"
description: "The paper addresses automated essay scoring (ATS) without the hand-crafted linguistic features that earlier systems depended on and that had to be…"
authors: ["Dimitrios Alikaniotis", "Helen Yannakoudakis", "Marek Rei"]
year: 2016
venue: "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/P16-1068/"
doi: "10.18653/v1/p16-1068"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/alikaniotis-2016-automatic-text-scoring-neural-networks/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/alikaniotis-2016-automatic-text-scoring-neural-networks/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2016](https://md.research.elamur.ai/free/en/papers/2016/) / Automatic Text Scoring Using Neural Networks

# Automatic Text Scoring Using Neural Networks

**Authors:** Dimitrios Alikaniotis, [Helen Yannakoudakis](https://md.research.elamur.ai/free/en/authors/helen-yannakoudakis/), [Marek Rei](https://md.research.elamur.ai/free/en/authors/marek-rei/) · **Year:** 2016 · **Venue:** Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/P16-1068/) · [PDF](https://aclanthology.org/P16-1068.pdf) · [DOI 10.18653/v1/p16-1068](https://doi.org/10.18653/v1/p16-1068) · [OpenAlex](https://openalex.org/W2440757793)

**Citations:** 183 citations (openalex-snapshot · 2026-10-14) · [74](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper addresses automated essay scoring (ATS) without the hand-crafted linguistic features that earlier systems depended on and that had to be retuned for each prompt, genre or domain. The authors extend the Collobert & Weston (C&W) embedding network, following Tang (2015). In addition to separating real n-grams from corrupted ones, the network gets a second output unit that regresses the essay's score. The resulting score-specific word embeddings (SSWEs) capture both a word's context and its relation to essay quality. Essays are then encoded with uni- or bi-directional LSTMs that predict a holistic score. Experiments use the Kaggle ASAP essays written by middle-school students. With only tokenization as preprocessing, SSWE + LSTM beats competing systems, including feature-engineered ones, on correlation measures and Cohen's κ. The authors also propose a gradient-based visualization that shows which words the model treats as good or poor usage. This matters because it moves essay scoring toward a domain-agnostic approach that can still be inspected and used for learner feedback.

## Key points
- Data: the Kaggle/Hewlett Automated Student Assessment Prize (ASAP) collection, about 20k texts, of which roughly 60% carry marks.
- The SSWE network has two output units. One gives a C&W-style hinge loss over E corrupted sequences, and the other gives a mean-squared-error regression onto the essay score. A weight α mixes the two losses, and both gradients update the embedding matrix.
- Hyperparameters were found with Bayesian Optimization. The best configuration was D=200, H=100, n=9, E=200, α=0.1, LSTM size 10 and dropout-like r=0.5. Raising α, which shifts weight toward the context objective, clearly hurt results.
- To check whether length drives the scores, they correlated gold scores with essay length on the test set. The correlation was modest (r=0.3, ρ=0.44), suggesting length is not doing the heavy lifting.
- In the visualizations, the model favours correctly placed punctuation and long-distance dependencies and catches POS errors. It struggles with proper names and produces some false negatives.

## Interesting ideas
- **Task-supervised embeddings:** Adding a downstream-label regression head to an embedding objective pushes apart words that share contexts but differ in "quality." For example, misspellings move away from correct forms while staying contextually close. The same trick could suit any task where word choice signals the label, such as readability or register detection.
- **Pseudo-score gradient probing:** You feed the trained model the maximum and minimum possible scores as fake targets. The size of the loss gradient at each input word then shows how far that word would need to shift. Contrasting the two results gives a per-token quality map, a cheap interpretability tool for any regression model over sequences.
- **Controlling for a trivial predictor:** Correlating gold labels with a known shortcut feature (here, text length) is a simple sanity check. It shows whether a model's strong numbers could come from a confound.
- **Bayesian Optimization over hyperparameters:** Beyond reaching good settings in fewer trials than grid search, the search revealed which parameters (α, E, LSTM size) actually matter. The tuning run thus doubles as an analysis of the design.
- **Extending probes to phrases:** The same gradient test can score a phrase or sentence by probing the LSTM state after its final token. This partly addresses the limitation that per-word gradients are only computed at essay end.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

74 papers of our library cite it (of the 140884 whose bibliographies we read); 3 of them have a page here, 71 not yet.

- [Automated Scoring of Arabic Text Using Large Language Models: A Literature Review](https://md.research.elamur.ai/free/en/papers/dahimi-2026-automated-scoring-arabic-text-using/)
- [Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring](https://md.research.elamur.ai/free/en/papers/dong-2017-attention-based-recurrent-convolutional-neural/)
- [A Neural Approach to Automated Essay Scoring](https://md.research.elamur.ai/free/en/papers/taghipour-2016-neural-approach-automated-essay-scoring/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{alikaniotis2016automatic,
  title = {{Automatic Text Scoring Using Neural Networks}},
  author = {Alikaniotis, Dimitrios and Yannakoudakis, Helen and Rei, Marek},
  booktitle = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2016},
  doi = {10.18653/v1/p16-1068},
  url = {https://doi.org/10.18653/v1/p16-1068}
}
```

- [← previous Automatic Sarcasm Detection: A Survey](https://md.research.elamur.ai/free/en/papers/joshi-2016-automatic-sarcasm-detection-survey/)
- [next → BCCWJ-DepPara: A Syntactic Annotation Treebank on the ‘Balanced Corpus of Contemporary Written Japanese'](https://md.research.elamur.ai/free/en/papers/asahara-2016-bccwj-deppara-syntactic-annotation-treebank/)
- [↑ back to papers of 2016](https://md.research.elamur.ai/free/en/papers/2016/)

## References

42 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Alex Graves. 2012. Supervised Sequence Labelling with Recurrent Neural Networks. Springer Berlin Heidelberg. [find](https://scholar.google.com/scholar?q=Alex+Graves%2E+2012%2E+Supervised+Sequence+Labelling+with+Recurrent+Neural+Networks%2E+Springer+Berlin+Heidelberg%2E)
2. Yigal Attali and Jill Burstein. 2006. Automated essay scoring with e-Rater v.2.0. Journal of Technology, Learning, and Assessment, 4(3):1–30. [find](https://scholar.google.com/scholar?q=Yigal+Attali+and+Jill+Burstein%2E+2006%2E+Automated+essay+scoring+with+e%2DRater+v%2E2%2E0%2E+Journal+of+Technology%2C+Learning%2C+and+Assessment%2C+4%283%29%3A1%E2%80%9330%2E)
3. Michael U. Gutmann and Aapo Hyvärinen. 2012. Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics. J. Mach. Learn. Res., 13:307–361, February. [find](https://scholar.google.com/scholar?q=Michael+U%2E+Gutmann+and+Aapo+Hyva%CC%88rinen%2E+2012%2E+Noise%2Dcontrastive+estimation+of+unnormalized+statistical+models%2C+with+applications+to+natural+image+statistics%2E+J%2E+Mach%2E+Learn%2E+Res%2E%2C+13%3A307%E2%80%93361%2C+February)
4. Ted Briscoe, John Carroll, and Rebecca Watson. 2006. The second release of the RASP system. In Proceedings of the COLING/ACL, volume 6. [find](https://scholar.google.com/scholar?q=Ted+Briscoe%2C+John+Carroll%2C+and+Rebecca+Watson%2E+2006%2E+The+second+release+of+the+RASP+system%2E+In+Proceedings+of+the+COLING%2FACL%2C+volume+6%2E) [our card](https://md.research.elamur.ai/free/en/papers/briscoe-2006-second-release-rasp-system/)
5. Ted Briscoe, Ben Medlock, and Øistein E. Andersen. 2010. Automated assessment of ESOL free text examinations. Technical Report UCAM-CL-TR-790, University of Cambridge, Computer Laboratory, nov. [find](https://scholar.google.com/scholar?q=Ted+Briscoe%2C+Ben+Medlock%2C+and+%C3%98istein+E%2E+Andersen%2E+2010%2E+Automated+assessment+of+ESOL+free+text+examinations%2E+Technical+Report+UCAM%2DCL%2DTR%2D790%2C+University+of+Cambridge%2C+Computer+Laboratory%2C+nov%2E)
6. Karl Moritz Hermann, Tom Koisk, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015. Teaching machines to read and comprehend. Jun. [find](https://scholar.google.com/scholar?q=Karl+Moritz+Hermann%2C+Tom+Koisk%2C+Edward+Grefenstette%2C+Lasse+Espeholt%2C+Will+Kay%2C+Mustafa+Suleyman%2C+and+Phil+Blunsom%2E+2015%2E+Teaching+machines+to+read+and+comprehend%2E+Jun%2E) [our card](https://md.research.elamur.ai/free/en/papers/hermann-2015-teaching-machines-read-comprehend/)
7. S Hochreiter and J Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735– 1780. [find](https://scholar.google.com/scholar?q=S+Hochreiter+and+J+Schmidhuber%2E+1997%2E+Long+short%2Dterm+memory%2E+Neural+computation%2C+9%288%29%3A1735%E2%80%93+1780%2E)
8. Ciprian Chelba, Tomáš Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, and Tony Robinson. 2013. One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling. In arXiv preprint. [find](https://scholar.google.com/scholar?q=Ciprian+Chelba%2C+Toma%CC%81s%CC%8C+Mikolov%2C+Mike+Schuster%2C+Qi+Ge%2C+Thorsten+Brants%2C+Phillipp+Koehn%2C+and+Tony+Robinson%2E+2013%2E+One+Billion+Word+Benchmark+for+Measuring+Progress+in+Statistical+Language+Modeling%2E+In+) [our card](https://md.research.elamur.ai/free/en/papers/chelba-2013-one-billion-word-benchmark-measuring/)
9. Beata Beigman Klebanov and Michael Flor. 2013. Word association profiles and their use for automated scoring of essays. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pages 1148–1158. [find](https://scholar.google.com/scholar?q=Beata+Beigman+Klebanov+and+Michael+Flor%2E+2013%2E+Word+association+profiles+and+their+use+for+automated+scoring+of+essays%2E+In+Proceedings+of+the+51st+Annual+Meeting+of+the+Association+for+Computational+L)
10. YY Chen, CL Liu, TH Chang, and CH Lee. 2010. An Unsupervised Automated Essay Scoring System. IEEE Intelligent Systems, pages 61–67. [find](https://scholar.google.com/scholar?q=YY+Chen%2C+CL+Liu%2C+TH+Chang%2C+and+CH+Lee%2E+2010%2E+An+Unsupervised+Automated+Essay+Scoring+System%2E+IEEE+Intelligent+Systems%2C+pages+61%E2%80%9367%2E)
11. Ronan Collobert and Jason Weston. 2008. A unified architecture for natural language processing: deep neural networks with multitask learning. Proceedings of the Twenty-Fifth international conference on Machine Learning, pages 160–167, July. [find](https://scholar.google.com/scholar?q=Ronan+Collobert+and+Jason+Weston%2E+2008%2E+A+unified+architecture+for+natural+language+processing%3A+deep+neural+networks+with+multitask+learning%2E+Proceedings+of+the+Twenty%2DFifth+international+conference+o)
12. Thomas K. Landauer, Darrell Laham, and Peter W. Foltz. 2003. Automated scoring and annotation of essays with the Intelligent Essay Assessor. In M.D. Shermis and J.C. Burstein, editors, Automated essay scoring: A cross-disciplinary perspective, pages 87– 112. [find](https://scholar.google.com/scholar?q=Thomas+K%2E+Landauer%2C+Darrell+Laham%2C+and+Peter+W%2E+Foltz%2E+2003%2E+Automated+scoring+and+annotation+of+essays+with+the+Intelligent+Essay+Assessor%2E+In+M%2ED%2E+Shermis+and+J%2EC%2E+Burstein%2C+editors%2C+Automated+essay)
13. Ronan Collobert, Jason Weston, Leon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. Mar. [find](https://scholar.google.com/scholar?q=Ronan+Collobert%2C+Jason+Weston%2C+Leon+Bottou%2C+Michael+Karlen%2C+Koray+Kavukcuoglu%2C+and+Pavel+Kuksa%2E+2011%2E+Natural+language+processing+%28almost%29+from+scratch%2E+Mar%2E) [our card](https://md.research.elamur.ai/free/en/papers/collobert-2011-natural-language-processing-almost-scratch/)
14. Quoc V. Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. May. [find](https://scholar.google.com/scholar?q=Quoc+V%2E+Le+and+Tomas+Mikolov%2E+2014%2E+Distributed+representations+of+sentences+and+documents%2E+May%2E) [our card](https://md.research.elamur.ai/free/en/papers/le-2014-distributed-representations-sentences-documents/)
15. Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y. Ng. 2009. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. Proceedings of the 26th Annual International Conference on Machine Learning ICML 09. [find](https://scholar.google.com/scholar?q=Honglak+Lee%2C+Roger+Grosse%2C+Rajesh+Ranganath%2C+and+Andrew+Y%2E+Ng%2E+2009%2E+Convolutional+deep+belief+networks+for+scalable+unsupervised+learning+of+hierarchical+representations%2E+Proceedings+of+the+26th+Annu)
16. Scott Crossley, Laura K Allen, Erica L Snow, and Danielle S McNamara. 2015. Pssst... textual features... there is more to automatic essay scoring than just you! In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge, pages 203–207. ACM. [find](https://scholar.google.com/scholar?q=Scott+Crossley%2C+Laura+K+Allen%2C+Erica+L+Snow%2C+and+Danielle+S+McNamara%2E+2015%2E+Pssst%2E%2E%2E+textual+features%2E%2E%2E+there+is+more+to+automatic+essay+scoring+than+just+you%21+In+Proceedings+of+the+Fifth+Internation)
17. Deryle Lonsdale and D. Strong-Krause. 2003. Automated rating of ESL essays. In Proceedings of the HLT-NAACL 2003 Workshop: Building Educational Applications Using Natural Language Processing. [find](https://scholar.google.com/scholar?q=Deryle+Lonsdale+and+D%2E+Strong%2DKrause%2E+2003%2E+Automated+rating+of+ESL+essays%2E+In+Proceedings+of+the+HLT%2DNAACL+2003+Workshop%3A+Building+Educational+Applications+Using+Natural+Language+Processing%2E)
18. Yann N. Dauphin, Harm de Vries, and Yoshua Bengio. 2015. Equilibrated adaptive learning rates for non-convex optimization. Feb. [find](https://scholar.google.com/scholar?q=Yann+N%2E+Dauphin%2C+Harm+de+Vries%2C+and+Yoshua+Bengio%2E+2015%2E+Equilibrated+adaptive+learning+rates+for+non%2Dconvex+optimization%2E+Feb%2E)
19. Misha Denil, Alban Demiraj, Nal Kalchbrenner, Phil Blunsom, and Nando de Freitas. 2014. Modelling, visualising and summarising documents with a single convolutional neural network. Jun. [find](https://scholar.google.com/scholar?q=Misha+Denil%2C+Alban+Demiraj%2C+Nal+Kalchbrenner%2C+Phil+Blunsom%2C+and+Nando+de+Freitas%2E+2014%2E+Modelling%2C+visualising+and+summarising+documents+with+a+single+convolutional+neural+network%2E+Jun%2E)
20. Danielle S McNamara, Scott A Crossley, Rod D Roscoe, Laura K Allen, and Jianmin Dai. 2015. A hierarchical classification approach to automated essay scoring. Assessing Writing, 23:35–59. [find](https://scholar.google.com/scholar?q=Danielle+S+McNamara%2C+Scott+A+Crossley%2C+Rod+D+Roscoe%2C+Laura+K+Allen%2C+and+Jianmin+Dai%2E+2015%2E+A+hierarchical+classification+approach+to+automated+essay+scoring%2E+Assessing+Writing%2C+23%3A35%E2%80%9359%2E)
21. Semire Dikli. 2006. An overview of automated scoring of essays. Journal of Technology, Learning, and Assessment, 5(1). [find](https://scholar.google.com/scholar?q=Semire+Dikli%2E+2006%2E+An+overview+of+automated+scoring+of+essays%2E+Journal+of+Technology%2C+Learning%2C+and+Assessment%2C+5%281%29%2E)
22. Tomáš Mikolov, Stefan Kombrink, Anoop Deoras, Lukáš Burget, and Jan Černocký. 2011. RNNLM-Recurrent neural network language modeling toolkit. In ASRU 2011 Demo Session. [find](https://scholar.google.com/scholar?q=Toma%CC%81s%CC%8C+Mikolov%2C+Stefan+Kombrink%2C+Anoop+Deoras%2C+Luka%CC%81s%CC%8C+Burget%2C+and+Jan+C%CC%8Cernocky%CC%81%2E+2011%2E+RNNLM%2DRecurrent+neural+network+language+modeling+toolkit%2E+In+ASRU+2011+Demo+Session%2E)
23. S. Elliot. 2003. Intellimetric TM : From here to validity. In M. D. Shermis and J. Burnstein, editors, Automated Essay Scoring: A Cross-Disciplinary Perspective, pages 71–86. Lawrence Erlbaum Associates. [find](https://scholar.google.com/scholar?q=S%2E+Elliot%2E+2003%2E+Intellimetric+TM+%3A+From+here+to+validity%2E+In+M%2E+D%2E+Shermis+and+J%2E+Burnstein%2C+editors%2C+Automated+Essay+Scoring%3A+A+Cross%2DDisciplinary+Perspective%2C+pages+71%E2%80%9386%2E+Lawrence+Erlbaum+Associat)
24. Tomas Mikolov, I Sutskever, K Chen, G S Corrado, and Jeffrey Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, pages 3111–3119. [find](https://scholar.google.com/scholar?q=Tomas+Mikolov%2C+I+Sutskever%2C+K+Chen%2C+G+S+Corrado%2C+and+Jeffrey+Dean%2E+2013%2E+Distributed+representations+of+words+and+phrases+and+their+compositionality%2E+In+Advances+in+Neural+Information+Processing+Syste)
25. Noura Farra, Swapna Somasundaran, and Jill Burstein. 2015. Scoring persuasive essays using opinions and their targets. In Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 64–74. [find](https://scholar.google.com/scholar?q=Noura+Farra%2C+Swapna+Somasundaran%2C+and+Jill+Burstein%2E+2015%2E+Scoring+persuasive+essays+using+opinions+and+their+targets%2E+In+Proceedings+of+the+Tenth+Workshop+on+Innovative+Use+of+NLP+for+Building+Educat)
26. Ellis B. Page. 1967. Grading essays by computer: progress report. In Proceedings of the Invitational Conference on Testing Problems, pages 87–100. [find](https://scholar.google.com/scholar?q=Ellis+B%2E+Page%2E+1967%2E+Grading+essays+by+computer%3A+progress+report%2E+In+Proceedings+of+the+Invitational+Conference+on+Testing+Problems%2C+pages+87%E2%80%93100%2E)
27. Helen Yannakoudakis and Ronan Cummins. 2015. Evaluating the performance of automated text scoring systems. In Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics (ACL). [find](https://scholar.google.com/scholar?q=Helen+Yannakoudakis+and+Ronan+Cummins%2E+2015%2E+Evaluating+the+performance+of+automated+text+scoring+systems%2E+In+Proceedings+of+the+Tenth+Workshop+on+Innovative+Use+of+NLP+for+Building+Educational+Applic)
28. Ellis B. Page. 1968. The use of the computer in analyzing student essays. International Review of Education, 14(2):210–225, June. [find](https://scholar.google.com/scholar?q=Ellis+B%2E+Page%2E+1968%2E+The+use+of+the+computer+in+analyzing+student+essays%2E+International+Review+of+Education%2C+14%282%29%3A210%E2%80%93225%2C+June%2E)
29. E.B. Page. 2003. Project essay grade: PEG. In M.D. Shermis and J.C. Burstein, editors, Automated essay scoring: A cross-disciplinary perspective, pages 43– 54. [find](https://scholar.google.com/scholar?q=E%2EB%2E+Page%2E+2003%2E+Project+essay+grade%3A+PEG%2E+In+M%2ED%2E+Shermis+and+J%2EC%2E+Burstein%2C+editors%2C+Automated+essay+scoring%3A+A+cross%2Ddisciplinary+perspective%2C+pages+43%E2%80%93+54%2E)
30. Helen Yannakoudakis, Ted Briscoe, and Ben Medlock. 2011. A new dataset and method for automatically grading ESOL texts. In The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, 19-24 June, 2011, Portland, Oregon, USA, pa… [find](https://scholar.google.com/scholar?q=Helen+Yannakoudakis%2C+Ted+Briscoe%2C+and+Ben+Medlock%2E+2011%2E+A+new+dataset+and+method+for+automatically+grading+ESOL+texts%2E+In+The+49th+Annual+Meeting+of+the+Association+for+Computational+Linguistics%3A+Hum) [our card](https://md.research.elamur.ai/free/en/papers/yannakoudakis-2011-new-dataset-method-automatically-grading/)
31. L.M. Rudner and Tahung Liang. 2002. Automated essay scoring using Bayes’ theorem. The Journal of Technology, Learning and Assessment, 1(2):3–21. [find](https://scholar.google.com/scholar?q=L%2EM%2E+Rudner+and+Tahung+Liang%2E+2002%2E+Automated+essay+scoring+using+Bayes%E2%80%99+theorem%2E+The+Journal+of+Technology%2C+Learning+and+Assessment%2C+1%282%29%3A3%E2%80%9321%2E)
32. Keisuke Sakaguchi, Michael Heilman, and Nitin Madnani. 2015. Effective feature integration for automated short answer scoring. In Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications. [find](https://scholar.google.com/scholar?q=Keisuke+Sakaguchi%2C+Michael+Heilman%2C+and+Nitin+Madnani%2E+2015%2E+Effective+feature+integration+for+automated+short+answer+scoring%2E+In+Proceedings+of+the+Tenth+Workshop+on+Innovative+Use+of+NLP+for+Buildin)
33. M Shermis and B Hammer. 2012. Contrasting state-of-the-art automated scoring of essays: analysis. Technical report, The University of Akron and Kaggle. [find](https://scholar.google.com/scholar?q=M+Shermis+and+B+Hammer%2E+2012%2E+Contrasting+state%2Dof%2Dthe%2Dart+automated+scoring+of+essays%3A+analysis%2E+Technical+report%2C+The+University+of+Akron+and+Kaggle%2E)
34. Mark D Shermis. 2015. Contrasting state-of-the-art in the machine scoring of short-form constructed responses. Educational Assessment, 20(1):46–65. [find](https://scholar.google.com/scholar?q=Mark+D+Shermis%2E+2015%2E+Contrasting+state%2Dof%2Dthe%2Dart+in+the+machine+scoring+of+short%2Dform+constructed+responses%2E+Educational+Assessment%2C+20%281%29%3A46%E2%80%9365%2E)
35. Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013. Deep inside convolutional networks: Visualising image classification models and saliency maps. 12. [find](https://scholar.google.com/scholar?q=Karen+Simonyan%2C+Andrea+Vedaldi%2C+and+Andrew+Zisserman%2E+2013%2E+Deep+inside+convolutional+networks%3A+Visualising+image+classification+models+and+saliency+maps%2E+12%2E) [our card](https://md.research.elamur.ai/free/en/papers/simonyan-2013-deep-inside-convolutional-networks-visualising/)
36. D.D.K. Sleator and D. Templerley. 1995. Parsing English with a link grammar. Proceedings of the 3rd International Workshop on Parsing Technologies, ACL. [find](https://scholar.google.com/scholar?q=D%2ED%2EK%2E+Sleator+and+D%2E+Templerley%2E+1995%2E+Parsing+English+with+a+link+grammar%2E+Proceedings+of+the+3rd+International+Workshop+on+Parsing+Technologies%2C+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/parsers-1993-sleator-parsing-english-link-grammar/)
37. Jasper Snoek, Hugo Larochelle, and Ryan P. Adams. 2012. Practical bayesian optimization of machine learning algorithms. Jun. [find](https://scholar.google.com/scholar?q=Jasper+Snoek%2C+Hugo+Larochelle%2C+and+Ryan+P%2E+Adams%2E+2012%2E+Practical+bayesian+optimization+of+machine+learning+algorithms%2E+Jun%2E)
38. Swapna Somasundaran, Jill Burstein, and Martin Chodorow. 2014. Lexical chaining for measuring discourse coherence quality in test-taker essays. In COLING, pages 950–961. [find](https://scholar.google.com/scholar?q=Swapna+Somasundaran%2C+Jill+Burstein%2C+and+Martin+Chodorow%2E+2014%2E+Lexical+chaining+for+measuring+discourse+coherence+quality+in+test%2Dtaker+essays%2E+In+COLING%2C+pages+950%E2%80%93961%2E) [our card](https://md.research.elamur.ai/free/en/papers/somasundaran-2014-lexical-chaining-measuring-discourse-coherence/)
39. Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. Sep. [find](https://scholar.google.com/scholar?q=Ilya+Sutskever%2C+Oriol+Vinyals%2C+and+Quoc+V%2E+Le%2E+2014%2E+Sequence+to+sequence+learning+with+neural+networks%2E+Sep%2E)
40. Kai Sheng Tai, Richard Socher, and Christopher D. Manning. 2015. Improved semantic representations from tree-structured long short-term memory networks. Feb. [find](https://scholar.google.com/scholar?q=Kai+Sheng+Tai%2C+Richard+Socher%2C+and+Christopher+D%2E+Manning%2E+2015%2E+Improved+semantic+representations+from+tree%2Dstructured+long+short%2Dterm+memory+networks%2E+Feb%2E)
41. Duyu Tang. 2015. Sentiment-specific representation learning for document-level sentiment analysis. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining - WSDM '15. Association for Computing Machinery (ACM). [find](https://scholar.google.com/scholar?q=Duyu+Tang%2E+2015%2E+Sentiment%2Dspecific+representation+learning+for+document%2Dlevel+sentiment+analysis%2E+In+Proceedings+of+the+Eighth+ACM+International+Conference+on+Web+Search+and+Data+Mining+%2D+WSDM+%2715%2E+A)
42. D. M. Williamson. 2009. A framework for implementing automated scoring. Technical report, Educational Testing Service. [find](https://scholar.google.com/scholar?q=D%2E+M%2E+Williamson%2E+2009%2E+A+framework+for+implementing+automated+scoring%2E+Technical+report%2C+Educational+Testing+Service%2E)