# MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms

@inproceedings{Amini2019MathQATI, title={MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms}, author={Aida Amini and Saadia Gabriel and Shanchuan Lin and Rik Koncel-Kedziorski and Yejin Choi and Hannaneh Hajishirzi}, booktitle={NAACL}, year={2019} }

We introduce a large-scale dataset of math word problems and an interpretable neural math problem solver that learns to map problems to operation programs. [...] Key Method We additionally introduce a neural sequence-to-program model enhanced with automatic problem categorization. Our experiments show improvements over competitive baselines in our MathQA as well as the AQuA dataset. The results are still significantly lower than human performance indicating that the dataset poses new challenges for future… Expand

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#### References

SHOWING 1-10 OF 27 REFERENCES

Using Intermediate Representations to Solve Math Word Problems

- Computer Science
- ACL
- 2018

This work uses a sequence-to-sequence model with a novel attention regularization term to generate the intermediate forms, then executes them to obtain the final answers, and proposes an iterative labeling framework for learning by leveraging supervision signals from both equations and answers. Expand

Mapping to Declarative Knowledge for Word Problem Solving

- Computer Science
- Transactions of the Association for Computational Linguistics
- 2018

Declarative rules which govern the translation of natural language description of these concepts to math expressions are developed, and a framework for incorporating such declarative knowledge into word problem solving is presented. Expand

A Meaning-Based Statistical English Math Word Problem Solver

- Computer Science
- NAACL
- 2018

Experimental results show that the MeSys approach outperforms existing systems on both benchmark datasets and the noisy dataset, which demonstrates that the proposed approach understands the meaning of each quantity in the text more. Expand

Neural Math Word Problem Solver with Reinforcement Learning

- Computer Science
- COLING
- 2018

Experimental results show that the copy and alignment mechanism is effective to address the two issues and Reinforcement learning leads to better performance than maximum likelihood on this task; and the neural model is complementary to the feature-based model and their combination significantly outperforms the state-of-the-art results. Expand

Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems

- Computer Science
- ACL
- 2017

Experimental results show that indirect supervision of program learning via answer rationales is a promising strategy for inducing arithmetic programs. Expand

How well do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation

- Computer Science
- ACL
- 2016

A large-scale dataset which is more than 9 times the size of previous ones, and contains many more problem types, and is trained to automatically extract problem answers from the answer text provided by CQA users, which significantly reduces human annotation cost. Expand

Modeling Math Word Problems with Augmented Semantic Networks

- Computer Science
- NLDB
- 2012

This work proposes a model based on augmented semantic networks to represent the mathematical structure behind word problems that is able to understand and solve mathematical text problems from German primary school books and could be extended to other languages by exchanging the language model in the natural language processing module. Expand

MAWPS: A Math Word Problem Repository

- Computer Science
- NAACL
- 2016

MAWPS allows for the automatic construction of datasets with particular characteristics, providing tools for tuning the lexical and template overlap of a dataset as well as for filtering ungrammatical problems from web-sourced corpora. Expand

Deep Neural Solver for Math Word Problems

- Computer Science
- EMNLP
- 2017

Experiments conducted on a large dataset show that the RNN model and the hybrid model significantly outperform state-of-the-art statistical learning methods for math word problem solving. Expand

Annotating Derivations: A New Evaluation Strategy and Dataset for Algebra Word Problems

- Computer Science
- EACL
- 2017

A new evaluation for automatic solvers for algebra word problems is proposed, which can identify mistakes that existing evaluations overlook, and derivation annotations can be semi-automatically added to existing datasets. Expand