HMS: A Hierarchical Solver with Dependency-Enhanced Understanding for Math Word Problem

By Xin Lin, Zhenya Huang, Hongke Zhao, Enhong Chen, Qi Liu, Hao Wang, Shijin Wang. 2021

72 citations recorded by the source corpus.

Automatically solving math word problems is a crucial task for exploring the intelligence levels of machines in the general AI domain. It is highly challenging since it requires not only natural language understanding but also mathematical expression inference. Existing solutions usually explore sequence-to-sequence models to generate expressions, where the problems are simply encoded sequentially. However, such models are generally far from enough for understanding problems as similar to humans and lead to incorrect answers. To this end, in this paper, we propose a novel Hierarchical Math Solver (HMS) to make deep understanding and exploitation of problems. In problem understanding, imitating human reading habits, we propose a hierarchical word-clause-problem encoder. Specifically, we first split each problem into several clauses and learn problem semantics from the local clause level to the global problem level. Then, in clause understanding, we propose a dependency-based module to enhance clause semantics with the dependency structure of the problem. Next, in expression inference, we propose a novel tree-based decoder to generate the mathematical expression for the answer. In the decoder, we apply a hierarchical attention mechanism to enhance the problem semantics with context from different levels, and a pointer-generator network to guide the model to copy existing information and infer extra knowledge. Extensive experimental results on two widely used datasets demonstrate that HMS achieves not only better answers but also more reasonable inference.

Subfields: Artificial Intelligence, Information Systems, 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. Wei Dang Lu
2. Zhanming Jie
3. Jierui Li
4. Bryan Perozzi
5. Chengqi Zhang
6. William L. Hamilton

In Information Systems

1. Wei Dang Lu
2. Zhanming Jie
3. Jierui Li

In Natural Language Processing

1. Wei Dang Lu
2. Zhanming Jie
3. Jierui Li

Citations in the corpus

• 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): “, 2021b) MWP Generation Seq2Tree BiLSTM Tree 4 Explicitly incorporating values HMS (Lin et al., 2021) MWP Generation Seq2Tree GRU Tree 4 A word-clause-problem encoder LBF (Hong et al.”