“A theme-rewriting approach for generating algebra word problems,”
Open accessAbstract
Texts present coherent stories that have a particular theme or overall setting, for example science fiction or western.In this paper, we present a text generation method called rewriting that edits existing human-authored narratives to change their theme without changing the underlying story.We apply the approach to math word problems, where it might help students stay more engaged by quickly transforming all of their homework assignments to the theme of their favorite movie without changing the math concepts that are being taught.Our rewriting method uses a twostage decoding process, which proposes new words from the target theme and scores the resulting stories according to a number of factors defining aspects of syntactic, semantic, and thematic coherence.Experiments demonstrate that the final stories typically represent the new theme well while still testing the original math concepts, outperforming a number of baselines.We also release a new dataset of human-authored rewrites of math word problems in several themes.
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Preliminary work in automatic MWP generation take a template-based approach, where an existing problem is generalized into a template, and a solution space fitting this template is explored to generate new problems.
“Preliminary work in automatic MWP generation take a template-based approach, where an existing problem is generalized into a template, and a solution space fitting this template is explored to generate new problems (Deane and Sheehan, 2003; Polozov et al., 2015; Koncel-Kedziorski et al., 2016).”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as Koncel-Kedziorski et al., 2016
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Cites
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568 · see citing works
WordNet: a lexical database for English
Miller, G. A. (1995) · Communications of the ACM · doi:10.1145/219717.219748 · Cited by 14,015 · see citing works
The Stanford CoreNLP Natural Language Processing Toolkit
Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S. J., & McClosky, D. (2014) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.3115/v1/p14-5010 · Cited by 7,171 · see citing works
Kronenfeld, D. B. (1978) · Language · doi:10.2307/412850 · Cited by 3,094 · see citing works
Incorporating non-local information into information extraction systems by Gibbs sampling
Finkel, J. R., Grenager, T., & Manning, C. D. (2005) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1219840.1219885 · Cited by 3,028 · see citing works
Scripts, Plans, Goals, and Understanding: An Inquiry into Human Knowledge Structures
Hunt, E. B., Schank, R. C., & Abelson, R. P. (1979) · The American Journal of Psychology · doi:10.2307/1421499 · Cited by 2,967 · see citing works
A Neural Attention Model for Abstractive Sentence Summarization
Rush, A. M., Chopra, S., & Weston, J. (2015) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1044 · Cited by 2,879 · see citing works
Meteor Universal: Language Specific Translation Evaluation for Any Target Language
Denkowski, M., & Lavie, A. (2014) · WMT@ACL · doi:10.3115/v1/w14-3348 · Cited by 2,108 · see citing works
5.7 The WORDNET lexical database of English
Ooi, V. B. Y. (2019) · Edinburgh University Press eBooks · doi:10.1515/9781474471459-050 · Cited by 2,034 · see citing works
A Fast and Accurate Dependency Parser using Neural Networks
Chen, D., & Manning, C. D. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1082 · Cited by 1,810 · see citing works
KenLM: Faster and Smaller Language Model Queries
Heafield, K. (2011) · Cited by 1,145 · see citing works
Dependency-Based Word Embeddings
Levy, O., & Goldberg, Y. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-2050 · Cited by 1,091 · see citing works
Collecting Highly Parallel Data for Paraphrase Evaluation
Chen, D. J., & Dolan, W. B. (2011) · Cited by 898 · see citing works
Solving General Arithmetic Word Problems
Roy, S., & Roth, D. (2016) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1202 · Cited by 702 · see citing works
Ganitkevitch, J., Durme, B. V., & Callison-Burch, C. (2013) · Cited by 659 · see citing works
MAWPS: A Math Word Problem Repository
Koncel-Kedziorski, R., Roy, S., Amini, A., Kushman, N., & Hajishirzi, H. (2016) · North American Chapter of the Association for Computational Linguistics · doi:10.18653/v1/n16-1136 · Cited by 551 · see citing works
Optimizing Statistical Machine Translation for Text Simplification
Xu, W. H., Napoles, C., Pavlick, E., Chen, Q. Z., & Callison-Burch, C. (2016) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00107 · Cited by 524 · see citing works
Unsupervised learning of narrative schemas and their participants
Chambers, N., & Jurafsky, D. (2009) · doi:10.3115/1690219.1690231 · Cited by 408 · see citing works
Learning to Automatically Solve Algebra Word Problems
Kushman, N., Zettlemoyer, L., Barzilay, R., & Artzi, Y. (2014) · Annual Meeting of the Association for Computational Linguistics · doi:10.3115/v1/p14-1026 · Cited by 400 · see citing works
A Monolingual Tree-based Translation Model for Sentence Simplification
Zhu, Z., Darmstadt, T. U., Bernhard, D., & Gurevych, I. (2010) · TUbilio (Technical University of Darmstadt) · Cited by 334 · see citing works
Beyond SumBasic: Task-focused summarization with sentence simplification and lexical expansion
Vanderwende, L., Suzuki, H., Brockett, C., & Nenkova, A. (2007) · Information Processing & Management · doi:10.1016/j.ipm.2007.01.023 · Cited by 310 · see citing works
Learning to Solve Arithmetic Word Problems with Verb Categorization
Hosseini, M. J., Hajishirzi, H., Etzioni, O., & Kushman, N. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1058 · Cited by 299 · see citing works
The metanovel: writing stories by computer.
Meehan, J. (1976) · Defense Technical Information Center (DTIC) · Cited by 288 · see citing works
Syntactic Simplification and Text Cohesion
Siddharthan, A. (2006) · Research on Language and Computation · doi:10.1007/s11168-006-9011-1 · Cited by 275 · see citing works
Solving General Arithmetic Word Problems
Roy, S., & Roth, D. (2016) · arXiv · doi:10.48550/arxiv.1608.01413 · Cited by 275 · cited 2×
“tomatically solving math word problems. Specific topics include number word problems (Shi et al., 2015), logic puzzle problems (Mitra and Baral, 2015), arithmetic word problems (Hosseini et al., 2014; Roy and Roth, 2015), algebra word problems (Kushman et al., 2014; Zhou et al., 2015; Koncel-Kedziorski et al., 2015a; R…”
BackgroundSentence Simplification by Monolingual Machine Translation
Wubben, S., van den Bosch, A. P. J., & Krahmer, E. (2012) · Research portal (Tilburg University) · Cited by 237 · see citing works
Parsing Algebraic Word Problems into Equations
Koncel-Kedziorski, R., Hajishirzi, H., Sabharwal, A., Etzioni, O., & Ang, S. D. (2015) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00160 · Cited by 233 · see citing works
Learning to Simplify Sentences with Quasi-Synchronous Grammar and Integer Programming
Woodsend, K., & Lapata, M. (2011) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 210 · see citing works
Individual interest as context in expository text and mathematical word problems
Renninger, K. A., Ewen, L., & Lasher, A. K. (2002) · Learning and Instruction · doi:10.1016/s0959-4752(01)00012-3 · Cited by 209 · see citing works
Automatically Solving Number Word Problems by Semantic Parsing and Reasoning
Shi, S., Wang, Y., Lin, C.-Y., Liu, X., & Rui, Y. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1135 · Cited by 204 · see citing works
A fast and portable realizer for text generation systems
Lavoie, B., & Rambow, O. (1997) · Conference on Applied Natural Language Processing · doi:10.3115/974557.974596 · Cited by 202 · see citing works
Story Generation with Crowdsourced Plot Graphs
Li, B., Lee-Urban, S., Johnston, G., & Riedl, M. O. (2013) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v27i1.8649 · Cited by 199 · see citing works
Minstrel: a computer model of creativity and storytelling
Turner, R. S. (1993) · Cited by 198 · see citing works
What Happens Next? Event Prediction Using a Compositional Neural Network Model
Granroth-Wilding, M., & Clark, S. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10344 · Cited by 191 · see citing works
Learning Statistical Scripts with LSTM Recurrent Neural Networks
Pichotta, K., & Mooney, R. J. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10347 · Cited by 179 · see citing works
Solving Geometry Problems: Combining Text and Diagram Interpretation
Seo, M., Hajishirzi, H., Farhadi, A., Etzioni, O., & Malcolm, C. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1171 · Cited by 158 · see citing works
How well do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation
Huang, D.-Q., Shi, S., Lin, C.-Y., Yin, J., & Ma, W.-Y. (2016) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p16-1084 · Cited by 158 · see citing works
The role of rewording and context personalization in the solving of mathematical word problems.
Davis-Dorsey, J., Ross, S. M., & Morrison, G. R. (1991) · Journal of Educational Psychology · doi:10.1037/0022-0663.83.1.61 · Cited by 133 · see citing works
A Dataset of Syntactic-Ngrams over Time from a Very Large Corpus of English Books
Goldberg, Y., & Orwant, J. (2013) · Cited by 133 · see citing works
For the sake of simplicity: Unsupervised extraction of lexical simplifications from WikipediaPreprint
Yatskar, M., Pang, B., Danescu-Niculescu-Mizil, C., & Lee, L. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1008.1986 · Cited by 127 · see citing works
Dependency tree based sentence compression
Filippova, K., & Strube, M. (2008) · doi:10.3115/1708322.1708329 · Cited by 125 · see citing works
Improving Text Simplification Language Modeling Using Unsimplified Text Data
Kauchak, D. (2013) · Cited by 125 · see citing works
Expanding teacher work roles: a resource for retention or a recipe for overwork?
Bartlett, L. (2004) · Journal of Education Policy · doi:10.1080/0268093042000269144 · Cited by 117 · see citing works
Sentence Simplification for Semantic Role Labeling
Vickrey, D., & Koller, D. (2008) · Cited by 113 · see citing works
A Corpus and Evaluation Framework for Deeper Understanding of Commonsense StoriesPreprint
Mostafazadeh, N., Chambers, N., He, X., Parikh, D., Batra, D., Vanderwende, L., Kohli, P., & Allen, J. F. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.01696 · Cited by 111 · see citing works
What Happens Next? Event Prediction Using a Compositional Neural Network Model
Granroth-Wilding, M., & Clark, S. C. (2016) · Apollo (University of Cambridge) · doi:10.17863/cam.13202 · Cited by 110 · see citing works
Learn to Solve Algebra Word Problems Using Quadratic Programming
Zhou, L., Dai, S., & Chen, L. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1096 · Cited by 109 · see citing works
Diagram Understanding in Geometry Questions
Seo, M. J., Hajishirzi, H., Farhadi, A., & Etzioni, O. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.9146 · Cited by 105 · see citing works
Aligning Sentences from Standard Wikipedia to Simple Wikipedia
Hwang, W., Hajishirzi, H., Mari Ostendorf, E., & Wu, W. (2015) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/v1/n15-1022 · Cited by 102 · see citing works
McIntyre, N., & Lapata, M. (2009) · doi:10.3115/1687878.1687910 · Cited by 95 · see citing works
Plot Induction and Evolutionary Search for Story Generation
McIntyre, N., & Lapata, M. (2010) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 80 · see citing works
Movie Script Summarization as Graph-based Scene Extraction
Gorinski, P. J., & Lapata, M. (2015) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/v1/n15-1113 · Cited by 72 · see citing works
Learning Sentential Paraphrases from Bilingual Parallel Corpora for Text-to-Text Generation
Ganitkevitch, J., Callison-Burch, C., Napoles, C., & Durme, B. V. (2011) · Cited by 59 · see citing works
Makebelieve: using commonsense knowledge to generate stories
Liu, H., & Singh, P. (2002) · National Conference on Artificial Intelligence · doi:10.5555/777092.777241 · Cited by 57 · see citing works
Equation Parsing : Mapping Sentences to Grounded Equations
Roy, S., Upadhyay, S., & Roth, D. (2016) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1117 · Cited by 50 · see citing works
Personalized mathematical word problem generation
Polozov, O., O’Rourke, E., Smith, A. M., Zettlemoyer, L., Gulwani, S., & Popović, Z. (2015) · Cited by 42 · see citing works
WikiSimple: Automatic Simplification of Wikipedia Articles
Woodsend, K., & Lapata, M. (2011) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v25i1.7967 · Cited by 40 · see citing works
Learning to Automatically Solve Logic Grid Puzzles
Mitra, A., & Baral, C. R. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1118 · Cited by 27 · see citing works
Planning algorithms for interactive storytelling
Barros, L. M., & Musse, S. R. (2007) · Computers in entertainment · doi:10.1145/1236224.1236232 · Cited by 22 · see citing works
The role of rewording and context personalization in the solving of mathematical word problems.
Davis-Dorsey, J., Ross, S. M., & Morrison, G. R. (1991) · Journal of Educational Psychology · doi:10.1037//0022-0663.83.1.61 · Cited by 9 · see citing works
Flagged first, then by citations
Research works printed but not linked: 6
- Nathanael Chambers and Dan Jurafsky. 2009. Unsuper- vised Learning of Narrative Schemas and Their Par- ticipants. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th Inter- national Joint Conference on Natural Language Pro- cessing of the AFNLP: Volume 2-Volume 2, pages 602- 610. Association for Computational Linguistics.
- Janis M Hart. 1996. The Effect of Personalized Word Problems. Teaching Children Mathematics, 2(8):504- 505.
- Rik Koncel-Kedziorski, Hannaneh Hajishirzi, Ashish Sabharwal, Oren Etzioni, and Siena Ang. 2015b. Parsing algebraic word problems into equations. Transactions of the Association for Computational Linguistics, 3:585-597.
- Michael Lebowitz. 1987. Planning Stories. In Proceed- ings of the cognitive science society, Hillsdale, pages 234-242.
- Sarah Petersen and Mari Ostendorf. 2007. Text simpli- fication for langauge learners: A corpus analysis. In Proceedings of the Speech and Language Technology in Education Workshop (SLaTE).
- Karl Pichotta and Raymond J. Mooney. 2016. Learn- ing statistical scripts with LSTM recurrent neural net- works. In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, Arizona.
Cited by
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Jin, D., Jin, Z., Hu, Z., Vechtomova, O., & Mihalcea, R. F. (2021) · Computational Linguistics · doi:10.1162/coli_a_00426 · Cited by 192 · see citing works
The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers
Zhang, D., Wang, L., Zhang, L., Dai, B. T., & Shen, H. T. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2914054 · Cited by 142 · see citing works
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Wang, D., Jojić, N., Brockett, C., & Nyberg, E. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1228 · Cited by 64 · see citing works
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Liu, T., Fang, Q., Ding, W., Li, H., Wu, Z., & Liu, Z. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.348 · Cited by 29 · see citing works
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Qin, L., Shwartz, V., West, P., Bhagavatula, C., Hwang, J. D., Bras, R. J. L., Bosselut, A., & Choi, Y. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.58 · Cited by 47 · see citing works
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Zhou, Z., Ning, M., Wang, Q., Yao, J., Wang, W., Huang, X., & Huang, K. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.705 · Cited by 27 · see citing works
Paper Abstract Writing through Editing Mechanism
Wang, Q., Zhou, Z., Huang, L., Whitehead, S., Zhang, B., Ji, H., & Knight, K. K. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-2042 · Cited by 23 · see citing works
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Jiao, Y., Shridhar, K., Cui, P., Zhou, W., & Sachan, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-36272-9_39 · Cited by 19 · see citing works
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Denny, P., Sarsa, S., Hellas, A., & Leinonen, J. (2022) · arXiv · doi:10.48550/arxiv.2211.04715 · Cited by 21 · see citing works
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Xu, Y., Smeets, R., & Rafael Bidarra, E. (2021) · International Journal of Serious Games · doi:10.17083/ijsg.v8i2.396 · Cited by 16 · see citing works
Qin, L., Shwartz, V., West, P., Bhagavatula, C., Hwang, J. D., Bras, R. J. L., Bosselut, A., & Choi, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.05906 · Cited by 16 · see citing works
Mori, Y., Yamane, H., Ushiku, Y., & Harada, T. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.03.006 · Cited by 15 · see citing works
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Wu, Q., Zhang, Q., & Huang, X. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3155284 · Cited by 12 · see citing works
Exploring the Needs and Interests of Fifth Graders for Personalized Math Word Problem Generation
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A Multi-language Platform for Generating Algebraic Mathematical Word Problems
Liyanage, V. P., & Ranathunga, S. (2019) · IEEE International Conference on Industrial and Information Systems (ICIIS) · doi:10.1109/iciis47346.2019.9063354 · Cited by 8 · see citing works
Liyanage, V. P., & Ranathunga, S. (2020) · Language Resources and Evaluation · Cited by 8 · see citing works
Sun, J., Shi, W., Shen, X., Liu, S., Wei, L., & Wan, Q. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103037 · Cited by 6 · see citing works
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- Authors
- Koncel-Kedziorski, R., Konstas, I., Zettlemoyer, L., & Hajishirzi, H.
- Affiliations
- University of Washington (US)
- Venue
- Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
- Published
- 2016
- Pages
- 1617-1628
- OpenAlex
- W2532931541
- Cited by
- 30
- References
- 60
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 1 · 2023: 4 · 2022: 3 · 2021: 3 · 2020: 4 · 2019: 7 · 2018: 2 · 2017: 2
- FWCI
- 1.98
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Funders
- National Science Foundation (1616112)
- Allen Institute for Artificial Intelligence
- Defense Advanced Research Projects Agency (FA8750-13-2-0008, FA8750)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Keywords
- algebra word problems, text rewriting, narrative coherence, two-stage decoding, thematic coherence, student engagement
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