Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems (2020.emnlp-main)
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| Challenge: | Existing models focus on one-unknown linear MWPs. |
| Approach: | They propose a universal expression tree-structured solver that integrates multiple expression trees underlying a MWP into a single expression tree. |
| Outcome: | The proposed method outperforms state-of-the-art models on a MWPs dataset and generates a universal expression tree explicitly by deciding which symbol to generate . |
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Non-Autoregressive Math Word Problem Solver with Unified Tree Structure (2023.emnlp-main)
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| Challenge: | Existing MWP solvers do not handle variants that can be derived via mathematical manipulation. |
| Approach: | They propose a non-autoregressive solver to present a solution expression and decode it from a given problem description. |
| Outcome: | The proposed solver is able to decode multiple expression variants and correct them . it is based on a unified tree structure and is available on Math23K and MAWPS. |
Structure-Unified M-Tree Coding Solver for Math Word Problem (2022.emnlp-main)
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| Challenge: | Existing models that take into account the binary tree structure of mathematical expressions have achieved better performance, but the output space is non-deterministic. |
| Approach: | They propose a Structure-Unified M-Tree Coding Solver which applies a tree with any M branches to unify the output structures. |
| Outcome: | The proposed model outperforms several state-of-the-art models under similar experimental conditions and performs much better under low-resource conditions. |
Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems (N19-1)
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| Challenge: | Existing methods to solve math word problems require accurate natural language understanding to bridge texts and math expressions. |
| Approach: | They propose a neural approach to automatically solve math word problems by operating symbols according to their semantic meanings in texts. |
| Outcome: | The proposed model outperforms state-of-the-art models and the best non-retrieval-based models over 10% accuracy in a Math23K dataset. |
Compositional Mathematical Encoding for Math Word Problems (2023.findings-acl)
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| Challenge: | Existing MWP encoders work in a unimodal setting and map problem description to latent representation, then for decoding. |
| Approach: | They propose a Compositional Math Word Problem Solver which maps problem description to latent representation and decodes it in an interactive way. |
| Outcome: | Extensive experiments show that the proposed model outperforms state-of-the-art models on public benchmarks. |
Tree-structured Decoding for Solving Math Word Problems (D19-1)
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| Challenge: | Existing approaches to solve math word problems do not consider an abstract syntax tree. |
| Approach: | They propose a tree-structured decoding method that generates an abstract syntax tree of an equation in a top-down manner and can stop during decoding without a redundant stop token. |
| Outcome: | The proposed method achieves state-of-the-art performance on the largest dataset on this task. |
Noun-MWP: Math Word Problems Meet Noun Answers (2022.coling-1)
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| Challenge: | Existing MWP solvers can handle Noun-MWPs, but they are not as efficient as other models. |
| Approach: | They propose a method to empower existing MWP solvers to handle Noun-MWPs. |
| Outcome: | The proposed model solves Noun-MWPs significantly better than other models and solves conventional MWP problems as well. |
Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks (2021.acl-long)
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| Challenge: | Existing solutions for math word problems lack explicit integration of math symbolic constraints, leading to unexplainable and unreasonable predictions. |
| Approach: | They propose a novel mathematical model that explicitly incorporates symbolic constraints by auxiliary tasks to enforce different symbolic reasoning. |
| Outcome: | The proposed solver incorporates symbolic constraints by auxiliary tasks to enforce different symbolic reasoning. |
Improving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning (2021.emnlp-main)
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| Challenge: | Existing algorithms for math word problems only capture word-level relationship and ignore to build hierarchical reasoning like the human being. |
| Approach: | They propose a Reasoning with Pre-trained Knowledge and Hierarchical Structure network that uses outside knowledge to build hierarchical reasoning like the human being. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two large-scale datasets and boosts performance. |
Math Word Problem Solving by Generating Linguistic Variants of Problem Statements (2023.acl-srw)
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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. |
Solving Math Word Problems with Multi-Encoders and Multi-Decoders (2020.coling-main)
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| Challenge: | Existing models that transform text descriptions into equation expressions only consider input/output objects as sequences, ignoring important structural information contained in text descriptions and equation expression. |
| Approach: | They propose a model that uses sequence-based encoders and graph-based decoders to enhance the representation of text descriptions and generate different equation expressions. |
| Outcome: | The proposed model outperforms existing state-of-the-art methods on a dataset with a n-word problem. |