A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving

By Qinzhuo Wu, Qi Zhang, Jinlan Fu, Xuanjing Huang. 2020

80 citations recorded by the source corpus.

With the advancements in natural language processing tasks, math word problem solving has received increasing attention. Previous methods have achieved promising results but ignore background common-sense knowledge not directly provided by the problem. In addition, during generation, they focus on local features while neglecting global information. To incorporate external knowledge and global expression information, we propose a novel knowledge-aware sequence-to-tree (KA-S2T) network in which the entities in the problem sequences and their categories are modeled as an entity graph. Based on this entity graph, a graph attention network is used to capture knowledge-aware problem representations. Further, we use a tree-structured decoder with a state aggregation mechanism to capture the long-distance dependency and global expression information. Experimental results on the Math23K dataset revealed that the KA-S2T model can achieve better performance than previously reported best results.

Subfields: Artificial Intelligence, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangliang Zhang
2. Zhenwen Liang
3. Jipeng Zhang
4. Nakul Verma
5. Iddo Drori
6. Lei Wang
7. Wei Dang Lu
8. Jierui Li

In Natural Language Processing

1. Zhenwen Liang
2. Xiangliang Zhang
3. Iddo Drori
4. Nakul Verma
5. Wei Dang Lu
6. Jierui Li
7. Jipeng Zhang
8. Lei Wang

Citations in the corpus

• MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers (2021, methodology): “, 2020), KAS2T (Wu et al., 2020), and NumS2T (Wu et al.”
• Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction (2022, background): “GroupAttn (Li et al., 2019) 69.5 66.9 mBERT-LSTM (Tan et al., 2021) 75.1 - BERT-BERT (Lan et al., 2021) - 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., ”
• A Survey of Deep Learning for Mathematical Reasoning (2022, unknown): “Various attention mechanisms have been studied to extract better representations, such as Group-ATT (Li et al., 2019) which uses different multi-head attention to extract various types of MWP features, and graph attention which is applied to extract knowledge-aware information in (Wu et al., 2020).”
• Solving Linear Algebra by Program Synthesis (2021, methodology): “Techniques have included sequence-tosequence and graph-to-tree Transformers which achieve around 80% on Math23k and MAWPS datasets (Koncel-Kedziorski et al., 2016; Li et al., 2019; Wang et al., 2019; Zhang et al., 2020; Li et al., 2020; Wu et al., 2020; Qin et al., 2020; Lan et al., 2021).”
• Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks (2021, unknown): “…2017; Liang et al., 2018a), to deep learning meth-ods (Ling et al., 2017; Wang et al., 2017, 2018b; Huang et al., 2018; Wang et al., 2018a; Xie and Sun, 2019; Wang et al., 2019; Zhang et al., 2020a,b; Qin et al., 2020; Shen and Jin, 2020; Wu et al., 2020; Chen et al., 2021; Hong et al., 2021a,b).”
• MWP-BERT: A Strong Baseline for Math Word Problems (2021, background): “Last, GTS is widely acknowledged as a strong baseline for MWP solving and all following studies like Graph2Tree , KA-S2T and Multi-ED are based on GTS .”
• Math Word Problem Solving with Explicit Numerical Values (2021, methodology): “Previous works (Xie and Sun, 2019; Liu et al., 2019; Wu et al., 2020) have confirmed that a sequence-to-tree model can better represent the expression structures than a sequence-to-sequence model, because a tree structured decoder can capture the global expression information and focus on the…”