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# Who cites «Learning to Solve Arithmetic Word Problems with Verb Categorization» · page 2

222 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/hosseini-2014-learning-solve-arithmetic-word-problems/)

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/hosseini-2014-learning-solve-arithmetic-word-problems/) · **2** · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/hosseini-2014-learning-solve-arithmetic-word-problems-3/)

1. [ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving](https://arxiv.org/abs/2309.17452) *— 2023 · arXiv:2309.17452*
2. [Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration](https://arxiv.org/abs/2311.08152) *— 2023 · arXiv:2311.08152*
3. [UniMath: A Foundational and Multimodal Mathematical Reasoner](https://aclanthology.org/2023.emnlp-main.440/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
4. [Unleashing the potential of prompt engineering for large language models](https://arxiv.org/abs/2310.14735) *— 2023 · arXiv:2310.14735 · Patterns 6(6) 101260 (2025)*
5. [World Models for Math Story Problems](https://aclanthology.org/2023.findings-acl.579/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
6. [Analytical Reasoning of Text](https://aclanthology.org/2022.findings-naacl.177/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
7. [ArMATH: a Dataset for Solving Arabic Math Word Problems](https://aclanthology.org/2022.lrec-1.37/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
8. [Automatic Generation of Socratic Subquestions for Teaching Math Word Problems](https://aclanthology.org/2022.emnlp-main.277/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
9. [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://arxiv.org/abs/2206.04615) *— 2022 · arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj*
10. [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/abs/2201.11903) *— 2022 · arXiv:2201.11903*
11. [Generating Sequences by Learning to Self-Correct](https://arxiv.org/abs/2211.00053) *— 2022 · arXiv:2211.00053*
12. [HAWP: a Dataset for Hindi Arithmetic Word Problem Solving](https://aclanthology.org/2022.lrec-1.373/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
13. [Investigating Math Word Problems using Pretrained Multilingual Language Models](https://aclanthology.org/2022.mathnlp-1.2/) *— 2022 · Proceedings of the 1st Workshop on Mathematical Natural Language Processing (MathNLP)*
14. [LILA: A Unified Benchmark for Mathematical Reasoning](https://aclanthology.org/2022.emnlp-main.392/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
15. [Large language models are zero-shot reasoners](https://arxiv.org/abs/2205.11916) *— 2022*
16. [NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks](https://aclanthology.org/2022.acl-long.246/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
17. [Practice Makes a Solver Perfect: Data Augmentation for Math Word Problem Solvers](https://aclanthology.org/2022.naacl-main.310/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
18. [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://arxiv.org/abs/2211.12588) *— 2022 · arXiv:2211.12588*
19. [Reasoning with Language Model Prompting: A Survey](https://arxiv.org/abs/2212.09597) *— 2022 · arXiv:2212.09597*
20. [Seeking Patterns, Not just Memorizing Procedures: Contrastive Learning for Solving Math Word Problems](https://aclanthology.org/2022.findings-acl.195/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
21. [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://arxiv.org/abs/2203.11171) *— 2022 · arXiv:2203.11171*
22. [Solving Quantitative Reasoning Problems with Language Models](https://arxiv.org/abs/2206.14858) *— 2022 · arXiv:2206.14858*
23. [Why are NLP Models Fumbling at Elementary Math? A Survey of Deep Learning based Word Problem Solvers](https://arxiv.org/abs/2205.15683) *— 2022 · arXiv 2205.15683*
24. [Generate & Rank: A Multi-task Framework for Math Word Problems](https://aclanthology.org/2021.findings-emnlp.195/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
25. [Mapping probability word problems to executable representations](https://aclanthology.org/2021.emnlp-main.294/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
26. [Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints](https://aclanthology.org/2021.emnlp-main.484/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
27. [Towards Tractable Mathematical Reasoning: Challenges, Strategies, and Opportunities for Solving Math Word Problems](https://arxiv.org/abs/2111.05364) *— 2021 · arXiv 2111.05364*
28. [A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers](https://aclanthology.org/2020.acl-main.92/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
29. [Graph-to-Tree Learning for Solving Math Word Problems](https://aclanthology.org/2020.acl-main.362/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
30. [Injecting Numerical Reasoning Skills into Language Models](https://aclanthology.org/2020.acl-main.89/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
31. [Solving Math Word Problems with Multi-Encoders and Multi-Decoders](https://aclanthology.org/2020.coling-main.262/) *— 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
32. [DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs](https://md.mova.elamur.ai/free/en/docs/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/) *— our card · 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*
33. [EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference](https://md.mova.elamur.ai/free/en/docs/papers/ravichander-2019-equate-benchmark-evaluation-framework-quantitative/) *— our card · 2019 · Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)*
34. [NumNet: Machine Reading Comprehension with Numerical Reasoning](https://aclanthology.org/D19-1251/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
35. [Quantity Tagger: A Latent-Variable Sequence Labeling Approach to Solving Addition-Subtraction Word Problems](https://aclanthology.org/P19-1517/) *— 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics*
36. [SemEval-2019 Task 10: Math Question Answering](https://aclanthology.org/S19-2153/) *— 2019 · Proceedings of the 13th International Workshop on Semantic Evaluation*
37. [Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems](https://aclanthology.org/N19-1272/) *— 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)*
38. [Template-Based Math Word Problem Solvers with Recursive Neural Networks](https://ojs.aaai.org/index.php/AAAI/article/view/4697) *— 2019 · AAAI 2019*
39. [Text2Math: End-to-end Parsing Text into Math Expressions](https://aclanthology.org/D19-1536/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
40. [The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers](https://arxiv.org/abs/1808.07290) *— 2019 · IEEE TPAMI 42(9), 2020 (arXiv 1808.07290)*
41. [Towards Generating Math Word Problems from Equations and Topics](https://aclanthology.org/W19-8661/) *— 2019 · Proceedings of the 12th International Conference on Natural Language Generation*
42. [Tree-structured Decoding for Solving Math Word Problems](https://aclanthology.org/D19-1241/) *— 2019 · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)*
43. [A Meaning-Based Statistical English Math Word Problem Solver](https://aclanthology.org/N18-1060/) *— 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)*
44. [Data-Driven Methods for Solving Algebra Word Problems](https://arxiv.org/abs/1804.10718) *— 2018 · arXiv 1804.10718*
45. [Mapping to Declarative Knowledge for Word Problem Solving](https://md.mova.elamur.ai/free/en/docs/papers/roy-2018-mapping-declarative-knowledge-word-problem/) *— our card · 2018*
46. [MathDQN: Solving Arithmetic Word Problems via Deep Reinforcement Learning](https://ojs.aaai.org/index.php/AAAI/article/view/11981) *— 2018 · AAAI 2018*
47. [Neural Math Word Problem Solver with Reinforcement Learning](https://aclanthology.org/C18-1018/) *— 2018 · Proceedings of the 27th International Conference on Computational Linguistics*
48. [Numeracy for Language Models: Evaluating and Improving their Ability to Predict Numbers](https://aclanthology.org/P18-1196/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
49. [Annotating Derivations: A New Evaluation Strategy and Dataset for Algebra Word Problems](https://aclanthology.org/E17-1047/) *— 2017 · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers*
50. [Beyond Sentential Semantic Parsing: Tackling the Math SAT with a Cascade of Tree Transducers](https://aclanthology.org/D17-1083/) *— 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing*
51. [Deep Neural Network based system for solving Arithmetic Word problems](https://aclanthology.org/I17-3017/) *— 2017 · Proceedings of the IJCNLP 2017, System Demonstrations*
52. [Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems](https://aclanthology.org/P17-1015/) *— 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
53. [Semantic Parsing of Pre-university Math Problems](https://aclanthology.org/P17-1195/) *— 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
54. [A Meaning-based English Math Word Problem Solver with Understanding, Reasoning and Explanation](https://aclanthology.org/C16-2032/) *— 2016 · Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations*
55. [A Tag-based English Math Word Problem Solver with Understanding, Reasoning and Explanation](https://aclanthology.org/N16-3014/) *— 2016 · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations*
56. [A Theme-Rewriting Approach for Generating Algebra Word Problems](https://aclanthology.org/D16-1168/) *— 2016 · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing*
57. [How well do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation](https://aclanthology.org/P16-1084/) *— 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
58. [Illinois Math Solver: Math Reasoning on the Web](https://aclanthology.org/N16-3011/) *— 2016 · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations*
59. [Learning To Use Formulas To Solve Simple Arithmetic Problems](https://md.mova.elamur.ai/free/en/docs/papers/mitra-2016-learning-use-formulas-solve-simple/) *— our card · 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
60. [Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems](https://aclanthology.org/D16-1029/) *— 2016 · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing*
61. [MAWPS: A Math Word Problem Repository](https://aclanthology.org/N16-1136/) *— 2016 · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
62. [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://aclanthology.org/D16-1264/) *— 2016 · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing*
63. [Aligning Sentences from Standard Wikipedia to Simple Wikipedia](https://aclanthology.org/N15-1022/) *— 2015 · Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
64. [Automatically Solving Number Word Problems by Semantic Parsing and Reasoning](https://aclanthology.org/D15-1135/) *— 2015 · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing*
65. [Designing a Tag-Based Statistical Math Word Problem Solver with Reasoning and Explanation](https://aclanthology.org/O15-1006/) *— 2015 · Proceedings of the 27th Conference on Computational Linguistics and Speech Processing (ROCLING 2015)*
66. [Learn to Solve Algebra Word Problems Using Quadratic Programming](https://aclanthology.org/D15-1096/) *— 2015 · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing*
67. [Learning Knowledge Graphs for Question Answering through Conversational Dialog](https://aclanthology.org/N15-1086/) *— 2015 · Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
68. [Learning to Automatically Solve Logic Grid Puzzles](https://aclanthology.org/D15-1118/) *— 2015 · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing*
69. [Parsing Algebraic Word Problems into Equations](https://aclanthology.org/Q15-1042/) *— 2015*
70. [Robust Subgraph Generation Improves Abstract Meaning Representation Parsing](https://aclanthology.org/P15-1095/) *— 2015 · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
71. [Solving General Arithmetic Word Problems](https://md.mova.elamur.ai/free/en/docs/papers/roy-2015-solving-general-arithmetic-word-problems/) *— our card · 2015 · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing*
72. [Solving Geometry Problems: Combining Text and Diagram Interpretation](https://aclanthology.org/D15-1171/) *— 2015 · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing*
73. [``A Spousal Relation Begins with a Deletion of engage and Ends with an Addition of divorce'''': Learning State Changing Verbs from Wikipedia Revision History](https://aclanthology.org/D15-1059/) *— 2015 · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing*
74. agarwal-2025-step-step-unmasking-parameter-efficient
75. ahmed-2025-bennumeval-benchmark-assess-llms-numerical
76. alenezi-2026-llm-prompting-techniques-arabic-language
77. athavale-2019-predicting-algorithm-classes-programming-word
78. baeumel-2025-disentangling-mathematical-reasoning-llms-methodological
79. bouchard-2016-learning-generate-textual-data
80. cao-2026-comol-efficient-mixture-lora-experts
81. cao-2026-moa-heterogeneous-mixture-adapters-parameter
82. cheng-2023-analyzing-chatgpt-s-mathematical-deficiencies
83. chu-2024-navigate-through-enigmatic-labyrinth-survey
84. er-2026-math-db-discourse-framework-mathematical
85. feng-2025-comoe-contrastive-representation-mixture-experts
86. gangwar-2026-integrating-arithmetic-learning-improves-mathematical
87. gou-2025-mitigating-demonstration-bias-through-global
88. griffith-2020-solving-arithmetic-word-problems-transformer
89. gu-2024-bottom-top-extending-potential-parameter
90. hao-2025-dynamic-collaboration-multi-language-models
91. jurczyk-2015-semantics-based-graph-approach-complex
92. kim-2023-athena-mathematical-reasoning-thought-expansion
93. kong-2024-better-zero-shot-reasoning-role
94. kowsher-2025-predicting-through-generation-why-generation
95. li-2025-glider-global-local-instruction-driven
96. li-2025-loracoe-improving-large-language-model
97. liang-2023-don-t-be-blind-questions
98. liu-2019-aifu-semeval-2019-task-10
99. liu-2024-making-large-language-models-better
100. liu-2024-mathbench-evaluating-theory-application-proficiency

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