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

By Yibin Shen, Cheqing Jin. 2020

69 citations recorded by the source corpus.

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.

Subfields: Natural Language Processing

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In Natural Language Processing

1. Zhenwen Liang
2. Xiangliang Zhang
3. Wei Dang Lu
4. Jierui Li
5. Jipeng Zhang
6. Lei Wang
7. Zhanming Jie
8. Jie Shao

Citations in the corpus

• MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers (2021, background): “Graph2Tree (Zhang et al., 2020b; Shen and Jin, 2020), and Pre-trained Language Models (Kim et al.”
• Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction (2022, background): “…- 76.6 Roberta-Roberta (Lan et al., 2021) - 76.9 S 2 T / G 2 T GTS (Xie and Sun, 2019) 75.6 74.3 KA-S2T† (Wu et al., 2020) 76.3 - MultiE&D (Shen and Jin, 2020) 78.4 76.9 Graph2Tree (Zhang et al., 2020) 77.4 75.5 NeuralSymbolic (Qin et al., 2021) - 75.7 NUMS2T† (Wu et al., 2021) 78.1 - HMS (Lin et…”
• Large Language Models are Better Reasoners with Self-Verification (2022, unknown): “Later, specialized encoder-decoder architectures were designed to improve reasoning performance (Shen and Jin, 2020; Zhu et al., 2022a).”
• A Survey of Deep Learning for Mathematical Reasoning (2022, unknown): “The graph-based information can also be embedded when encoding the input mathematical sequences (Zhang et al., 2020b; Shen and Jin, 2020; Li et al., 2020b; Wu et al., 2021a).”
• MWP-BERT: A Strong Baseline for Math Word Problems (2021, methodology): “To avoid the implementation error that may cause unreproducible results of baseline models, we reported the results of these baselines from the papers where they were published, as many previous papers (Zhang et al., 2020b; Shen and Jin, 2020) did.”