MWP-BERT: A Strong Baseline for Math Word Problems

By Zhenwen Liang, Jipeng Zhang, Jie Shao, Xiangliang Zhang. 2021

42 citations recorded by the source corpus.

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Citations in the corpus

Training Verifiers to Solve Math Word Problems (2021, background): “…specialized encoder-decoder architectures (Amini et al., 2019; Chiang and Chen, 2018; Xie and Sun, 2019; Chen et al., 2020; Li et al., 2020), with the strongest results often relying on large pretrained encoders from the BERT family (Chen et al., 2019; Kim et al., 2020; Liang et al., 2021).”
MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers (2021, methodology): “…including graph neural networks based (Li et al., 2020), template based (Lee et al., 2021), neural symbolic (Qin et al., 2021), pre-trained based (Liang et al., 2021), multilingual pre-trained based methods (Tan et al., 2021), and solvers using external information and signals (Liang and Zhang;…”
Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction (2022, methodology): “Applying pre-trained language models such as BERT (Devlin et al., 2019) was shown to signiﬁcantly beneﬁt the tree expression generation (Lan et al., 2021; Tan et al., 2021; Liang et al., 2021; Li et al., 2021; Shen et al., 2021).”
Continual Pre-training of Language Models for Math Problem Understanding with Syntax-Aware Memory Network (2022, methodology): “Numerous deep learning based methods have been proposed to tackle the MWP task, including Seq2Seq (Chiang and Chen, 2019; Li et al., 2019), Seq2Tree (Wang et al., 2019; Qin et al., 2020), and Pre-trained Language Models (Kim et al., 2020; Liang et al., 2021).”
Solving Linear Algebra by Program Synthesis (2021, background): “2% accuracy on the Math23k dataset (Liang et al., 2021) of elementary school math problems.”