Math Word Problem Solving by Generating Linguistic Variants of Problem Statements (2023.acl-srw)
Copied to clipboard
Syed Rifat Raiyan, Md Nafis Faiyaz, Shah Md. Jawad Kabir, Mohsinul Kabir, Hasan Mahmud, Md Kamrul Hasan
| Challenge: | Existing models for solving Math Word Problems depend on shallow heuristics and spurious correlations to derive the solution expressions. |
| Approach: | They propose a framework for MWP solvers based on generation of linguistic variants of problem text. |
| Outcome: | The proposed framework improves the mathematical reasoning and robustness of the proposed model. |
Similar Papers
Adversarial Examples for Evaluating Math Word Problem Solvers (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing MWP solvers do not understand language and its relation with numbers, and their accuracy is unclear. |
| Approach: | They propose two methods to generate adversarial attacks to evaluate the robustness of existing MWP solvers. |
| Outcome: | The proposed method reduces the accuracy of existing MWP solvers by over 40% on two benchmark datasets. |
Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for generating mathematical word problems are lacking in educational assessment. |
| Approach: | They propose an end-to-end neural model to generate diverse mathematical word problems from commonsense knowledge graph and equations. |
| Outcome: | The proposed model outperforms the SOTA models in terms of evaluation metrics and topic relevance. |
Generate & Rank: A Multi-task Framework for Math Word Problems (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies formalize MWP as a generation task but mathematical expressions are prone to minor mistakes. |
| Approach: | They propose a ranking task for math word problem (MWP) that learns from its own mistakes and distinguishes between correct and incorrect expressions. |
| Outcome: | The proposed model outperforms baselines on the classical Math23k dataset and is 7% higher than the state-of-the-art. |
MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing work on math word problem solvers replace real numbers with symbolic placeholders to focus on logic reasoning. |
| Approach: | They propose to inject numerical properties into symbolic placeholders with contextualized representation learning schema to solve number representation dilemma. |
| Outcome: | The proposed model can solve MWP problems on English and Chinese benchmarks. |
Seeking Diverse Reasoning Logic: Controlled Equation Expression Generation for Solving Math Word Problems (2022.aacl-short)
Copied to clipboard
| Challenge: | Existing methods to solve Math Word Problems rely on human annotation . empirical results suggest that our method universally improves the performance on single-unknown and multiple-un unknown benchmarks. |
| Approach: | They propose a controlled equation generation solver by leveraging a set of control codes to guide the model to consider certain reasoning logic and decode the corresponding equations expressions transformed from the human reference. |
| Outcome: | The proposed method improves performance on single-unknown and multiple-un unknown benchmarks with 13.2% accuracy on the challenging multiple-unequal datasets. |
Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to generate arithmetic math word problems are invalid or have unsatisfactory language quality. |
| Approach: | They propose a method for automatically generating arithmetic math word problems from equations and context. |
| Outcome: | The proposed approach improves language quality and mathematical validity on three real-world MWP datasets. |
Interpretable Math Word Problem Solution Generation via Step-by-step Planning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to solving math word problems focus on obtaining the correct answer. |
| Approach: | They propose a step-by-step planning approach for intermediate solution generation that strategically plans the generation of the next solution step based on the MWP and the previous solution steps. |
| Outcome: | The proposed approach improves the accuracy and interpretability of the solution on automatic metrics and human evaluation. |
Are NLP Models really able to Solve Simple Math Word Problems? (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing solvers for math word problems often achieve high performance on benchmark datasets . existing models rely on shallow heuristics to achieve high accuracy . |
| Approach: | They restrict their attention to English MWPs taught in grades four and lower . they propose a challenge dataset to test the accuracy of MWp solvers . |
| Outcome: | The proposed model can solve a large fraction of MWPs even with shallow heuristics . the proposed model is much lower on the challenge dataset SVAMP . |
A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers (2020.acl-main)
Copied to clipboard
| Challenge: | Existing MWP corpora are limited in language patterns and problem types . a new corpus of 2,305 MWps is proposed that is more diverse in terms of lexicon usage . |
| Approach: | They propose to use ASDiv to measure lexicon usage diversity of a given MWP corpus. |
| Outcome: | The proposed corpus covers more problem types and text patterns than existing corpora and reflects the true capability of solvers more faithfully. |
Solving Math Word Problems via Cooperative Reasoning induced Language Models (2023.acl-long)
Copied to clipboard
Xinyu Zhu, Junjie Wang, Lin Zhang, Yuxiang Zhang, Yongfeng Huang, Ruyi Gan, Jiaxing Zhang, Yujiu Yang
| Challenge: | Large-scale pre-trained language models (PLMs) can be used to solve math word problems, but they lack fast adaptivity as humans. |
| Approach: | They propose a cooperative reasoning-induced PLM for solving the math word problem . they use system 1 as the generator and system 2 as the verifier to generate reasoning paths . |
| Outcome: | The proposed model improves on several mathematical reasoning datasets and achieves 9.6% improvement over baselines. |