Text2Math: End-to-end Parsing Text into Math Expressions

By Yanyan Zou, Wei Dang Lu. 2019

40 citations recorded by the source corpus.

We propose Text2Math, a model for semantically parsing text into math expressions. The model can be used to solve different math related problems including arithmetic word problems and equation parsing problems. Unlike previous approaches, we tackle the problem from an end-to-end structured prediction perspective where our algorithm aims to predict the complete math expression at once as a tree structure, where minimal manual efforts are involved in the process. Empirical results on benchmark datasets demonstrate the efficacy of our approach.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, 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. Xiang Ren
2. Bill Yuchen Lin
3. Seyeon Lee
4. Rahul Khanna
5. Liang Wang
6. Rodrigo Nogueira
7. Mateusz Malinowski
8. Fengyuan Xu

In Computer Vision and Pattern Recognition

1. Xiang Ren
2. Bill Yuchen Lin
3. Henryk Michalewski
4. Rodrigo Nogueira
5. Mateusz Malinowski
6. Liang Wang
7. Fengyuan Xu
8. Rahul Khanna

In Natural Language Processing

1. Bill Yuchen Lin
2. Xiang Ren
3. Rodrigo Nogueira
4. Mateusz Malinowski
5. Liang Wang
6. Fengyuan Xu
7. Rahul Khanna
8. Sheng Zhong

Citations in the corpus

• Measuring and Improving BERT’s Mathematical Abilities by Predicting the Order of Reasoning. (2021, background): “(Bobrow, 1964; Char-niak, 1969; Shi et al., 2015; Ling et al., 2017; Amini et al., 2019; Parisotto et al., 2016; Wang et al., 2018; Zou and Lu, 2019) as well as datasets (Ling et al., 2017; Amini et al., 2019; Huang et al., 2016; Saxton et al., 2019) An interesting approach is proposed in (Rabe et…”
• Birds Have Four Legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models (2020, background): “Some works focus on incorporate algebra computation ability in PTLMs (Zou and Lu, 2019; Geva et al., 2020), thus making them able to answer math reasoning tasks such as MAWPS (Koncel-Kedziorski et al., 2016) and DROP (Dua et al., 2019).”
• Investigating the Limitations of Transformers with Simple Arithmetic Tasks (2021, background): “Similarly, better numerical skills can be achieved by augmenting input texts with pre-computed numerical computations (Andor et al., 2019) or by explicitly inferring mathematical equations from natural language text (Zou & Lu, 2019a;b; Li et al., 2019; Liu et al., 2019; Shi, 2020).”
• Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem (2020, methodology): “Inspired by the great success of Seq2Seq models in Neural Machine Translation, deep-learning based methods are intensively explored by researchers in the equation generation (Wang et al., 2017; Ling et al., 2017; Li et al., 2018, 2019; Zou and Lu, 2019; Xie and Sun, 2019).”
• Ape210K: A Large-Scale and Template-Rich Dataset of Math Word Problems (2020, background): “Another line of research applies semantic parsing (Jia and Liang, 2016) based approach to solve math word problems (Zou and Lu, 2019; Dong and Lapata, 2016; Shi et al., 2015).”
• Investigating the Limitations of the Transformers with Simple Arithmetic Tasks (2021, unknown): “Similarly, better numerical skills can be achieved by augmenting input texts with pre-computed numerical computations (Andor et al., 2019) or by explicitly inferring mathematical equations from natural language text (Zou & Lu, 2019a;b; Li et al., 2019; Liu et al., 2019; Shi, 2020).”