Fetching the paper…
Reading the bibliography…
One of the fundamental challenges towards building any intelligent tutoring system is its ability to automatically grade short student answers.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
A comparative evaluation of socratic versus didactic tutoring. In Proceedings of the Annual Meeting of the Cognitive Science Society , Vol. 23
Carolyn Penstein Rosé, Johanna D Moore, Kurt VanLehn, and David Allbritton. 2001 · 2001
Earlier work this paper cites.
Towards robust computerised marking of free-text responses. In Proceedings of the International Computer Assisted Assessment Conference
Tom Mitchell, Terry Russell, Peter Broomhead, and Nicola Aldridge. 2002 · 2002
Earlier work this paper cites.
Auto-marking 2: An update on the UCLES-Oxford University research into using computational linguistics to score short, free text responses
Jana Z Sukkarieh, Stephen G Pulman, and Nicholas Raikes. 2004 · 2004
Earlier work this paper cites.
Frustratingly Easy Domain Adaptation
Hal Daumé III. 2007 · 2007
Earlier work this paper cites.
Recognizing entailment in intelligent tutoring systems
Rodney D Nielsen, Wayne Ward, and James H Martin. 2009 · 2009
Earlier work this paper cites.
Learning to Grade Short Answer Questions using Semantic Similarity Measures and Dependency Graph Alignments. In Proceedings of the Annual Meeting of the Association for Computational Linguistics: Human Language Technologies . 752–762
Michael Mohler, Razvan C. Bunescu, and Rada Mihalcea. 2011 · 2011
Earlier work this paper cites.
Multi-domain neural network language model.. In INTERSPEECH , Vol. 13. 2182–2186
Tanel Alumäe. 2013 · 2013
Earlier work this paper cites.
SemEval-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge. In Proceedings of the NAACL-HLT International Workshop on Semantic Evaluation . 263–274
Myroslava O. Dzikovska, Rodney D. Nielsen, Chris Brew, Claudia Leacock, Danilo Giampiccolo, Luisa Bentivogli, Peter Clark, Ido Dagan, and Hoa Trang Dang. 2013 · 2013
Earlier work this paper cites.
ETS: Domain adaptation and stacking for short answer scoring. In Proceedings of the Joint Conference on Lexical and Computational Semantics , Vol. 2. 275–279
Michael Heilman and Nitin Madnani. 2013 · 2013
Earlier work this paper cites.
SOFTCARDINALITY: Hierarchical text overlap for student response analysis. In Proceedings of the Joint Conference on Lexical and Computational Semantics , Vol. 2. 280–284
Sergio Jimenez, Claudia Becerra, and Alexander Gelbukh. 2013 · 2013
Earlier work this paper cites.
CoMeT: Integrating different levels of linguistic modeling for meaning assessment. In Proceedings of the Joint Conference on Lexical and Computational Semantics , Vol. 2. 608–616
Niels Ott, Ramon Ziai, Michael Hahn, and Detmar Meurers. 2013 · 2013
Earlier work this paper cites.
GloVe: Global Vectors for Word Representation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing . 1532–1543
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
Cited alongside, same era.
Multi-Domain Recurrent Neural Network Language Model for Medical Speech Recognition. In Baltic HLT . 149–152
Ottokar Tilk and Tanel Alumäe. 2014 · 2014
Cited alongside, same era.
A large annotated corpus for learning natural language inference
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. 2015 · 2015
Cited alongside, same era.
Identifying patterns for short answer scoring using graph-based lexico-semantic text matching. In Proceedings of the NAACL Workshop on Innovative Use of NLP for Building Educational Applications . 97–106
Lakshmi Ramachandran, Jian Cheng, and Peter Foltz. 2015 · 2015
Cited alongside, same era.
Multi-task cross-lingual sequence tagging from scratch
Zhilin Yang, Ruslan Salakhutdinov, and William Cohen. 2016 · 2016
Later among the works it cites.
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data. In Proceedings of the Conference on Empirical Methods in Natural Language Processing . 670–680
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and Antoine Bordes. 2017 · 2017
Later among the works it cites.
Adversarial Multi-task Learning for Text Classification. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2017 · 2017
Later among the works it cites.
Deep multitask learning for semantic dependency parsing
Hao Peng, Sam Thomson, and Noah A Smith. 2017 · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Waleed Ammar, George Mulcaire, Miguel Ballesteros, Chris Dyer, and Noah A Smith. 2016 · 2016
Cited alongside, same era.
Frustratingly easy neural domain adaptation. In Proceedings of the International Conference on Computational Linguistics . 387–396
Young-Bum Kim, Karl Stratos, and Ruhi Sarikaya. 2016 · 2016
Cited alongside, same era.
How Transferable are Neural Networks in NLP Applications?
Lili Mou, Zhao Meng, Rui Yan, Ge Li, Yan Xu, Lu Zhang, and Zhi Jin. 2016 · 2016
Cited alongside, same era.
Multi-task multi-domain representation learning for sequence tagging
Nanyun Peng and Mark Dredze. 2016 · 2016
Cited alongside, same era.
Deep multi-task learning with low level tasks supervised at lower layers. In Proceedings of the Annual Meeting of the Association for Computational Linguisticss , Vol. 2. 231–235
Anders Søgaard and Yoav Goldberg. 2016 · 2016
Cited alongside, same era.
Fast and Easy Short Answer Grading with High Accuracy. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . 1070–1075
Md. Arafat Sultan, Cristobal Salazar, and Tamara Sumner. 2016 · 2016
Cited alongside, same era.
A neural approach to automated essay scoring. In Proceedings of the Conference on Empirical Methods in Natural Language Processing . 1882–1891
Kaveh Taghipour and Hwee Tou Ng. 2016 · 2016
Cited alongside, same era.
Investigating neural architectures for short answer scoring. In Proceedings of the NAACL Workshop on Innovative Use of NLP for Building Educational Applications . 159–168
Brian Riordan, Andrea Horbach, Aoife Cahill, Torsten Zesch, and Chong Min Lee. 2017 · 2017
Later among the works it cites.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R Bowman. 2017 · 2017
Later among the works it cites.
Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces
Isabelle Augenstein, Sebastian Ruder, and Anders Søgaard. 2018 · 2018
Later among the works it cites.
Multinomial Adversarial Networks for Multi-Domain Text Classification. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) , Vol. 1. 1226–1240
Xilun Chen and Claire Cardie. 2018 · 2018
Later among the works it cites.
Sentence Level or Token Level Features for Automatic Short Answer Grading?: Use Both. In Proceedings of the International Conference Artificial Intelligence in Education
Swarnadeep Saha, Tejas I. Dhamecha, Smit Marvaniya, Renuka Sindhgatta, and Bikram Sengupta. 2018 · 2018
Later among the works it cites.
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning
Sandeep Subramanian, Adam Trischler, Yoshua Bengio, and Christopher J. Pal. 2018 · 2018
Later among the works it cites.
Earth Mover’s Distance Pooling over Siamese LSTMs for Automatic Short Answer Grading. In Proceedings of the International Joint Conference on Artificial Intelligence . 2046–2052
Sachin Kumar, Soumen Chakrabarti, and Shourya Roy. 2017 · 2052
Closest in time.