---
title: "Who cites Learning to Solve Arithmetic Word Problems with…"
description: "The papers of the Elamur Research library that cite «Learning to Solve Arithmetic Word Problems with Verb Categorization», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/hosseini-2014-learning-solve-arithmetic-word-problems/cited-by/page/2/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/hosseini-2014-learning-solve-arithmetic-word-problems/cited-by/page/2/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Learning to Solve Arithmetic Word Problems with…](https://md.research.elamur.ai/free/en/papers/hosseini-2014-learning-solve-arithmetic-word-problems/) / cited by

# 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.research.elamur.ai/free/en/papers/hosseini-2014-learning-solve-arithmetic-word-problems/)

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

1. [A Survey of Deep Learning for Mathematical Reasoning](https://aclanthology.org/2023.acl-long.817/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
2. [ATHENA: Mathematical Reasoning with Thought Expansion](https://aclanthology.org/2023.emnlp-main.1014/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
3. [Analyzing ChatGPT”s Mathematical Deficiencies: Insights and Contributions](https://aclanthology.org/2023.rocling-1.22/) *— 2023 · Proceedings of the 35th Conference on Computational Linguistics and Speech Processing (ROCLING 2023)*
4. [Batch Prompting: Efficient Inference with Large Language Model APIs](https://aclanthology.org/2023.emnlp-industry.74/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track*
5. [Better Zero-Shot Reasoning with Role-Play Prompting](https://arxiv.org/abs/2308.07702) *— 2023 · arXiv:2308.07702*
6. [Better Zero-Shot Reasoning with Self-Adaptive Prompting](https://aclanthology.org/2023.findings-acl.216/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
7. [Chain of Thought Prompting Elicits Knowledge Augmentation](https://aclanthology.org/2023.findings-acl.408/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
8. [Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration](https://arxiv.org/abs/2310.00280) *— 2023 · arXiv:2310.00280*
9. [Deductive Verification of Chain-of-Thought Reasoning](https://arxiv.org/abs/2306.03872) *— 2023 · arXiv:2306.03872*
10. [Defining a New NLP Playground](https://aclanthology.org/2023.findings-emnlp.799/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
11. [Distilling Reasoning Capabilities into Smaller Language Models](https://aclanthology.org/2023.findings-acl.441/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
12. [Don”t be Blind to Questions: Question-Oriented Math Word Problem Solving](https://aclanthology.org/2023.ijcnlp-main.2/) *— 2023 · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
13. [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://arxiv.org/abs/2305.19118) *— 2023 · arXiv:2305.19118*
14. [Evaluating Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2310.19736) *— 2023 · arXiv:2310.19736*
15. [Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communication](https://aclanthology.org/2023.emnlp-main.936/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
16. [Generalizable Chain-of-Thought Prompting in Mixed-task Scenarios with Large Language Models](https://arxiv.org/abs/2310.06692) *— 2023 · arXiv:2310.06692*
17. [How Many Demonstrations Do You Need for In-context Learning?](https://aclanthology.org/2023.findings-emnlp.745/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
18. [Intégration du raisonnement numérique dans les modèles de langue : État de l”art et direction de recherche](https://aclanthology.org/2023.jeptalnrecital-coria.11/) *— 2023 · Actes de CORIA-TALN 2023. Actes de la 18e Conférence en Recherche d'Information et Applications (CORIA)*
19. [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://arxiv.org/abs/2302.06476) *— 2023 · arXiv:2302.06476*
20. [It Ain”t Over: A Multi-aspect Diverse Math Word Problem Dataset](https://aclanthology.org/2023.emnlp-main.927/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
21. [LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models](https://aclanthology.org/2023.emnlp-main.319/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
22. [Language Models can Solve Computer Tasks](https://arxiv.org/abs/2303.17491) *— 2023 · arXiv:2303.17491*
23. [Large Language Models are Better Reasoners with Self-Verification](https://aclanthology.org/2023.findings-emnlp.167/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
24. [Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation](https://aclanthology.org/2023.emnlp-main.889/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
25. [Let”s reward step by step: Step-Level reward model as the Navigators for Reasoning](https://arxiv.org/abs/2310.10080) *— 2023 · arXiv:2310.10080*
26. [MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning](https://arxiv.org/abs/2309.05653) *— 2023 · arXiv:2309.05653*
27. [Math Word Problem Solving by Generating Linguistic Variants of Problem Statements](https://aclanthology.org/2023.acl-srw.49/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)*
28. [Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future](https://arxiv.org/abs/2309.15402) *— 2023 · arXiv:2309.15402*
29. [Orca 2: Teaching Small Language Models How to Reason](https://arxiv.org/abs/2311.11045) *— 2023 · arXiv:2311.11045*
30. [Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts](https://aclanthology.org/2023.emnlp-main.169/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
31. [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](https://aclanthology.org/2023.acl-long.147/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
32. [Progressive-Hint Prompting Improves Reasoning in Large Language Models](https://arxiv.org/abs/2304.09797) *— 2023 · arXiv:2304.09797*
33. [Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution](https://arxiv.org/abs/2309.16797) *— 2023 · arXiv:2309.16797*
34. [R$^3$ Prompting: Review, Rephrase and Resolve for Chain-of-Thought Reasoning in Large Language Models under Noisy Context](https://aclanthology.org/2023.findings-emnlp.114/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
35. [Tab-CoT: Zero-shot Tabular Chain of Thought](https://aclanthology.org/2023.findings-acl.651/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
36. [TheoremQA: A Theorem-driven Question Answering Dataset](https://aclanthology.org/2023.emnlp-main.489/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
37. [Through the Lens of Core Competency: Survey on Evaluation of Large Language Models](https://arxiv.org/abs/2308.07902) *— 2023 · arXiv:2308.07902*
38. [ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving](https://arxiv.org/abs/2309.17452) *— 2023 · arXiv:2309.17452*
39. [Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration](https://arxiv.org/abs/2311.08152) *— 2023 · arXiv:2311.08152*
40. [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*
41. [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)*
42. [Verb Categorisation for Hindi Word Problem Solving](https://aclanthology.org/2023.icon-1.59/) *— 2023 · Proceedings of the 20th International Conference on Natural Language Processing (ICON)*
43. [World Models for Math Story Problems](https://aclanthology.org/2023.findings-acl.579/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
44. [Analytical Reasoning of Text](https://aclanthology.org/2022.findings-naacl.177/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
45. [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*
46. [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*
47. [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*
48. [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/abs/2201.11903) *— 2022 · arXiv:2201.11903*
49. [Generating Sequences by Learning to Self-Correct](https://arxiv.org/abs/2211.00053) *— 2022 · arXiv:2211.00053*
50. [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*
51. [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)*
52. [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*
53. [Large language models are zero-shot reasoners](https://arxiv.org/abs/2205.11916) *— 2022*
54. [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)*
55. [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*
56. [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://arxiv.org/abs/2211.12588) *— 2022 · arXiv:2211.12588*
57. [Reasoning with Language Model Prompting: A Survey](https://arxiv.org/abs/2212.09597) *— 2022 · arXiv:2212.09597*
58. [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*
59. [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://arxiv.org/abs/2203.11171) *— 2022 · arXiv:2203.11171*
60. [Solving Quantitative Reasoning Problems with Language Models](https://arxiv.org/abs/2206.14858) *— 2022 · arXiv:2206.14858*
61. [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*
62. [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*
63. [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*
64. [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*
65. [Towards Tractable Mathematical Reasoning: Challenges, Strategies, and Opportunities for Solving Math Word Problems](https://arxiv.org/abs/2111.05364) *— 2021 · arXiv 2111.05364*
66. [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*
67. [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*
68. [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*
69. [Recurrent Inference in Text Editing](https://aclanthology.org/2020.findings-emnlp.159/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
70. [Solving Arithmetic Word Problems Using Transformer and Pre-processing of Problem Texts](https://aclanthology.org/2020.icon-main.10/) *— 2020 · Proceedings of the 17th International Conference on Natural Language Processing (ICON)*
71. [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*
72. [AiFu at SemEval-2019 Task 10: A Symbolic and Sub-symbolic Integrated System for SAT Math Question Answering](https://aclanthology.org/S19-2154/) *— 2019 · Proceedings of the 13th International Workshop on Semantic Evaluation*
73. [DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs](https://md.research.elamur.ai/free/en/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)*
74. [EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/ravichander-2019-equate-benchmark-evaluation-framework-quantitative/) *— our card · 2019 · Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)*
75. [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)*
76. [Predicting Algorithm Classes for Programming Word Problems](https://aclanthology.org/D19-5511/) *— 2019 · Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)*
77. [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*
78. [SemEval-2019 Task 10: Math Question Answering](https://aclanthology.org/S19-2153/) *— 2019 · Proceedings of the 13th International Workshop on Semantic Evaluation*
79. [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)*
80. [Template-Based Math Word Problem Solvers with Recursive Neural Networks](https://ojs.aaai.org/index.php/AAAI/article/view/4697) *— 2019 · AAAI 2019*
81. [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)*
82. [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)*
83. [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*
84. [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)*
85. [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)*
86. [Data-Driven Methods for Solving Algebra Word Problems](https://arxiv.org/abs/1804.10718) *— 2018 · arXiv 1804.10718*
87. [EquGener: A Reasoning Network for Word Problem Solving by Generating Arithmetic Equations](https://aclanthology.org/Y18-1053/) *— 2018 · Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation*
88. [Mapping to Declarative Knowledge for Word Problem Solving](https://md.research.elamur.ai/free/en/papers/roy-2018-mapping-declarative-knowledge-word-problem/) *— our card · 2018*
89. [MathDQN: Solving Arithmetic Word Problems via Deep Reinforcement Learning](https://ojs.aaai.org/index.php/AAAI/article/view/11981) *— 2018 · AAAI 2018*
90. [Neural Math Word Problem Solver with Reinforcement Learning](https://aclanthology.org/C18-1018/) *— 2018 · Proceedings of the 27th International Conference on Computational Linguistics*
91. [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)*
92. [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*
93. [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*
94. [Deep Neural Network based system for solving Arithmetic Word problems](https://aclanthology.org/I17-3017/) *— 2017 · Proceedings of the IJCNLP 2017, System Demonstrations*
95. [Natural Language Programing with Automatic Code Generation towards Solving Addition-Subtraction Word Problems](https://aclanthology.org/W17-7519/) *— 2017 · Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017)*
96. [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)*
97. [Reasoning with Sets to Solve Simple Word Problems Automatically](https://aclanthology.org/W17-7524/) *— 2017 · Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017)*
98. [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)*
99. [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*
100. [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*

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