Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems

Qin, Jinghui; Lin, Lihui; Liang, Xiaodan; Zhang, Rumin

doi:10.18653/v1/2020.emnlp-main.309

Cited by 51 publications

(61 citation statements)

References 19 publications

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“…Many of the existing datasets are not suitable for our analysis as either they are in Chinese, e.g. Math23k (Wang et al, 2017) and HMWP (Qin et al, 2020), or have harder problem types, e.g. Dolphin18K (Huang et al, 2016b The performance of these models on both datasets is shown in Table 2.…”

Section: Datasets and Methodsmentioning

confidence: 99%

“…Recently, ASDiv (Miao et al, 2020) has been proposed to provide more diverse problems with annotations for equation, problem type and grade level. HMWP (Qin et al, 2020) Poliak et al (2018), andGururangan et al (2018). Rosenman et al (2020) identified shallow heuristics in a Relation Extraction dataset.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, researchers have started focusing on solving such MWPs, e.g. multiple-unknown linear word problems (Huang et al, 2016a), geometry (Sachan and Xing, 2017) and probability (Amini et al, 2019), believing that existing work can handle one-unknown arithmetic MWPs well (Qin et al, 2020). In this paper, we question the capabilities of the state-of-the-art (SOTA) methods to robustly solve even the simplest of MWPs suggesting that the above belief is not well-founded.…”

Section: Introductionmentioning

confidence: 96%

See 2 more Smart Citations

Are NLP Models really able to Solve Simple Math Word Problems?

Patel¹,

Bhattamishra²,

Goyal³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

108

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The problem of designing NLP solvers for math word problems (MWP) has seen sustained research activity and steady gains in the test accuracy. Since existing solvers achieve high performance on the benchmark datasets for elementary level MWPs containing one-unknown arithmetic word problems, such problems are often considered "solved" with the bulk of research attention moving to more complex MWPs. In this paper, we restrict our attention to English MWPs taught in grades four and lower. We provide strong evidence that the existing MWP solvers rely on shallow heuristics to achieve high performance on the benchmark datasets. To this end, we show that MWP solvers that do not have access to the question asked in the MWP can still solve a large fraction of MWPs. Similarly, models that treat MWPs as bag-ofwords can also achieve surprisingly high accuracy. Further, we introduce a challenge dataset, SVAMP, created by applying carefully chosen variations over examples sampled from existing datasets. The best accuracy achieved by state-of-the-art models is substantially lower on SVAMP, thus showing that much remains to be done even for the simplest of the MWPs.

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Section: Datasets and Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

See 1 more Smart Citation

Are NLP Models really able to Solve Simple Math Word Problems?

Patel¹,

Bhattamishra²,

Goyal³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

108

View full text Add to dashboard Cite

show abstract

“…Broadly there are two main lines of achieving interpretable solving steps for math problems. The first generates intermediate structural results of equation templates (Huang et al, 2017; , operational programs (Amini et al, 2019) and expression trees (Wang et al, 2018;Qin et al, 2020;Hong et al, 2021). The second line of work with a higher level of interpretability translates the math problems into symbolic language and conducts logical reasoning iteratively to predict the final results (Matsuzaki et al, 2017;Roy and Roth, 2018).…”

Section: Approaches For Geometry Problem Solvingmentioning

confidence: 99%

Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning

Lu¹,

Gong²,

Jiang³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

Self Cite

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Geometry problem solving has attracted much attention in the NLP community recently. The task is challenging as it requires abstract problem understanding and symbolic reasoning with axiomatic knowledge. However, current datasets are either small in scale or not publicly available. Thus, we construct a new largescale benchmark, Geometry3K, consisting of 3,002 geometry problems with dense annotation in formal language. We further propose a novel geometry solving approach with formal language and symbolic reasoning, called Interpretable Geometry Problem Solver (Inter-GPS). Inter-GPS first parses the problem text and diagram into formal language automatically via rule-based text parsing and neural object detecting, respectively. Unlike implicit learning in existing methods, Inter-GPS incorporates theorem knowledge as conditional rules and performs symbolic reasoning step by step. Also, a theorem predictor is designed to infer the theorem application sequence fed to the symbolic solver for the more efficient and reasonable searching path. Extensive experiments on the Geometry3K and GEOS datasets demonstrate that Inter-GPS achieves significant improvements over existing methods. 1

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“…To date, there exist limited literature on MWP generation. Most prior works focus on automatically answering MWPs, e.g., (Li et al, 2019(Li et al, , 2020Qin et al, 2020;Shi et al, 2015;Roy and Roth, 2015;Wu et al, 2020a) instead of generating them (Nandhini and Balasundaram, 2011;Williams, 2011;Polozov et al, 2015;Deane and Sheehan, 2003). Existing MWP generation methods also often generate MWPs that either are of unsatisfactory language quality or fail to preserve information on math equations and contexts that need to be embedded in them.…”

Section: Introductionmentioning

confidence: 99%

Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints

Wang¹,

Lan²,

Baraniuk³

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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We study the problem of generating arithmetic math word problems (MWPs) given a math equation that specifies the mathematical computation and a context that specifies the problem scenario. Existing approaches are prone to generating MWPs that are either mathematically invalid or have unsatisfactory language quality. They also either ignore the context or require manual specification of a problem template, which compromises the diversity of the generated MWPs. In this paper, we develop a novel MWP generation approach that leverages i) pre-trained language models and a context keyword selection model to improve the language quality of the generated MWPs and ii) an equation consistency constraint for math equations to improve the mathematical validity of the generated MWPs. Extensive quantitative and qualitative experiments on three realworld MWP datasets demonstrate the superior performance of our approach compared to various baselines.

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Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems

Cited by 51 publications

References 19 publications

Are NLP Models really able to Solve Simple Math Word Problems?

Are NLP Models really able to Solve Simple Math Word Problems?

Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning

Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints

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