Tree-structured Decoding for Solving Math Word Problems

Liu, Qianying; Guan, Wenyv; Li, Sujian; Kawahara, Daisuke

doi:10.18653/v1/d19-1241

Cited by 63 publications

(60 citation statements)

References 21 publications

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“…S2S (Wang et al, 2018a) is a standard bidirectional LSTM-based seq2seq model with an attention mechanism. RecursiveNN uses a recursive neural network on the predicted tree structure templates Tree-Decoder (Liu et al, 2019) is a seq2tree model with a tree structured decoder. The decoder generates each node based on its parent node and its sibling node.…”

Section: Baselinesmentioning

confidence: 99%

“…Math Word Problem Solving: In recent years, Seq2Seq has been widely used in math word problem solving tasks (Ling et al, 2017;Wang et al, 2017bWang et al, , 2018a. To better utilize expression structure information, recent studies have used Seq2Tree models (Liu et al, 2019;Zhang et al, 2020a). Xie and Sun (2019) proposed a tree structured decoder that uses a goal-driven approach to generate expression trees.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Math Word Problem Solving with Explicit Numerical Values

Wu¹,

Zhang²,

Wei³

et al. 2021

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

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In recent years, math word problem solving has received considerable attention and achieved promising results, but previous methods rarely take numerical values into consideration.Most methods treat the numerical values in the problems as number symbols, and ignore the prominent role of the numerical values in solving the problem. In this paper, we propose a novel approach called NumS2T, which enhances math word problem solving performance by explicitly incorporating numerical values into a sequence-to-tree network. In addition, a numerical properties prediction mechanism is used to capture the category and comparison information of numerals and measure their importance in global expressions. Experimental results on the Math23K and APE datasets demonstrate that our model achieves better performance than existing state-of-the-art models. 1

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Section: Baselinesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Math Word Problem Solving with Explicit Numerical Values

Wu¹,

Zhang²,

Wei³

et al. 2021

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

View full text Add to dashboard Cite

show abstract

“…The idea of the tree-based decoder was proposed in (Liu et al, 2019;Xie and Sun, 2019). They changed the pattern of sequence generation from left to right and followed the top-down decoding process.…”

Section: Related Workmentioning

confidence: 99%

“…Group-ATT added different functional multi-head attentions to the Seq2Seq framework. AST-Dec (Liu et al, 2019) used TreeLSTM to realize top-down decoding process. GTS (Xie and Sun, 2019) followed goal-driven tree structure.…”

Section: Baselinesmentioning

confidence: 99%

“…However, these models may generate invalid expressions since the sequencebased decoder cannot control the generation process. Based on the fact that each equation expression could be transformed into an abstract syntax tree (AST), some studies (Liu et al, 2019;Xie and Sun, 2019) changed the pattern of sequence generation from left to right and followed the top-down decoding process. Such tree-based decoders match the prefix order of AST.…”

Section: Introductionmentioning

confidence: 99%

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Solving Math Word Problems with Multi-Encoders and Multi-Decoders

Shen¹,

Jin²

2020

Proceedings of the 28th International Conference on Computational Linguistics

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Math word problems solving remains a challenging task where potential semantic and mathematical logic need to be mined from natural language. Although previous researches employ the Seq2Seq technique to transform text descriptions into equation expressions, most of them achieve inferior performance due to insufficient consideration in the design of encoder and decoder. Specifically, these models only consider input/output objects as sequences, ignoring the important structural information contained in text descriptions and equation expressions. To overcome those defects, a model with multi-encoders and multi-decoders is proposed in this paper, which combines sequence-based encoder and graph-based encoder to enhance the representation of text descriptions, and generates different equation expressions via sequence-based decoder and tree-based decoder. Experimental results on the dataset Math23K show that our model outperforms existing state-of-the-art methods.

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Does a language model “understand” high school math? A survey of deep learning based word problem solvers

Sundaram,

Gurajada,

Padmanabhan

et al. 2024

WIREs Data Min & Knowl

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From the latter half of the last decade, there has been a growing interest in developing algorithms for automatically solving mathematical word problems (MWP). It is a challenging and unique task that demands blending surface level text pattern recognition with mathematical reasoning. In spite of extensive research, we still have a lot to explore for building robust representations of elementary math word problems and effective solutions for the general task. In this paper, we critically examine the various models that have been developed for solving word problems, their pros and cons and the challenges ahead. In the last 2 years, a lot of deep learning models have recorded competing results on benchmark datasets, making a critical and conceptual analysis of literature highly useful at this juncture. We take a step back and analyze why, in spite of this abundance in scholarly interest, the predominantly used experiment and dataset designs continue to be a stumbling block. From the vantage point of having analyzed the literature closely, we also endeavor to provide a road‐map for future math word problem research.This article is categorized under: Technologies > Machine Learning Technologies > Artificial Intelligence Fundamental Concepts of Data and Knowledge > Knowledge Representation

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Tree-structured Decoding for Solving Math Word Problems

Cited by 63 publications

References 21 publications

Math Word Problem Solving with Explicit Numerical Values

Math Word Problem Solving with Explicit Numerical Values

Solving Math Word Problems with Multi-Encoders and Multi-Decoders

Does a language model “understand” high school math? A survey of deep learning based word problem solvers

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