Quantity Tagger: A Latent-Variable Sequence Labeling Approach to Solving Addition-Subtraction Word Problems

Zou, Yanyan; Lu, Wei

doi:10.18653/v1/p19-1517

Cited by 6 publications

(6 citation statements)

References 19 publications

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“…Besides, we borrow an MWP-specific sequence labeling task, quantity tagging (Zou and Lu, 2019b) ("QT"), to further compose MWP understanding evaluation settings. Quantity tagging (Zou and Lu, 2019a) is firstly proposed to solve MWP examples with only addition and subtraction operators in their solutions. Briefly speaking, this task requires the model to assign "+", "-" or "None" for every quantity in the problem description and can serve as an MWP understanding evaluation tool to examine the model's understanding of each variable's logic role in the reasoning flow.…”

Section: Modelmentioning

confidence: 99%

MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving

Liang¹,

Zhang²,

Wang³

et al. 2022

Findings of the Association for Computational Linguistics: NAACL 2022

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Math word problem (MWP) solving faces a dilemma in number representation learning. In order to avoid the number representation issue and reduce the search space of feasible solutions, existing works striving for MWP solving usually replace real numbers with symbolic placeholders to focus on logic reasoning. However, different from common symbolic reasoning tasks like program synthesis and knowledge graph reasoning, MWP solving has extra requirements in numerical reasoning. In other words, instead of the number value itself, it is the reusable numerical property that matters more in numerical reasoning. Therefore, we argue that injecting numerical properties into symbolic placeholders with contextualized representation learning schema can provide a way out of the dilemma in the number representation issue here. In this work, we introduce this idea to the popular pre-training language model (PLM) techniques and build MWP-BERT, an effective contextual number representation PLM. We demonstrate the effectiveness of our MWP-BERT on MWP solving and several MWP-specific understanding tasks on both English and Chinese benchmarks.

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Section: Modelmentioning

confidence: 99%

MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving

Liang¹,

Zhang²,

Wang³

et al. 2022

Findings of the Association for Computational Linguistics: NAACL 2022

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“…The earlier works on math word problems (MWPs) are mainly tested on small-scale datasets. These works can be broadly divided into statistical machine learning based Hosseini et al, 2014;Mitra and Baral, 2016;Roy and Roth, 2018;Zou and Lu, 2019a) and semantic parsing based (Shi et al, 2015;Koncel-Kedziorski et al, 2015;Roy and Roth, 2015;Huang et al, 2017;Zou and Lu, 2019b).…”

Section: Math Word Problems Solvingmentioning

confidence: 99%

Graph-to-Tree Learning for Solving Math Word Problems

Zhang¹,

Wang²,

Lee³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

109

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While the recent tree-based neural models have demonstrated promising results in generating solution expression for the math word problem (MWP), most of these models do not capture the relationships and order information among the quantities well. This results in poor quantity representations and incorrect solution expressions. In this paper, we propose Graph2Tree, a novel deep learning architecture that combines the merits of the graph-based encoder and tree-based decoder to generate better solution expressions. Included in our Graph2Tree framework are two graphs, namely the Quantity Cell Graph and Quantity Comparison Graph, which are designed to address limitations of existing methods by effectively representing the relationships and order information among the quantities in MWPs. We conduct extensive experiments on two available datasets. Our experiment results show that Graph2Tree outperforms the state-of-the-art baselines on two benchmark datasets significantly. We also discuss case studies and empirically examine Graph2Tree's effectiveness in translating the MWP text into solution expressions 1 .

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“…The MWP is the task of translating a short paragraph consisting with multiple short sentences into target mathematical equations. Previous approaches usually solve the MWP by using rulebased methods (Yuhui et al, 2010;Bakman, 2007), statistical machine learning methods (Kushman et al, 2014;Mitra and Baral, 2016;Roy and Roth, 2018;Zou and Lu, 2019), semantic parsing methods (Shi et al, 2015;Roy and Roth, 2015;Huang et al, 2017) and deep learning methods (Ling et al, 2017a;Wang et al, 2018b;Liu et al, 2019b;Wang et al, 2017;Zhang et al, 2020a). Recently, the deep learning based methods have been paid more attention for their significant improvement.…”

Section: Related Workmentioning

confidence: 99%

Improving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning

Yu¹,

Wen²,

Zheng³

et al. 2021

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

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The recent algorithms for math word problems (MWP) neglect to use outside knowledge not present in the problems. Most of them only capture the word-level relationship and ignore to build hierarchical reasoning like the human being for mining the contextual structure between words and sentences. In this paper, we propose a Reasoning with Pre-trained Knowledge and Hierarchical Structure (RPKHS) network, which contains a pre-trained knowledge encoder and a hierarchical reasoning encoder. Firstly, our pretrained knowledge encoder aims at reasoning the MWP by using outside knowledge from the pre-trained transformer-based models. Secondly, the hierarchical reasoning encoder is presented for seamlessly integrating the wordlevel and sentence-level reasoning to bridge the entity and context domain on MWP. Extensive experiments show that our RPKHS significantly outperforms state-of-the-art approaches on two large-scale commonly-used datasets, and boosts performance from 77.4% to 83.9% on Math23K, from 75.5 to 82.2% on Math23K with 5-fold cross-validation and from 83.7% to 89.8% on MAWPS. More extensive ablations are shown to demonstrate the effectiveness and interpretability of our proposed method.

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Quantity Tagger: A Latent-Variable Sequence Labeling Approach to Solving Addition-Subtraction Word Problems

Cited by 6 publications

References 19 publications

MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving

MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving

Graph-to-Tree Learning for Solving Math Word Problems

Improving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning

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