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
MWP-BERT: A Strong Baseline for Math Word Problems
TLDR
This work introduces MWP-BERT to obtain pre-trained token representations that capture the alignment between text description and mathematical logic and introduces a keywordbased prompt matching method to address the MWPs requiring common-sense knowledge. Expand
Reverse Operation based Data Augmentation for Solving Math Word Problems
TLDR
A reverse operation based data augmentation method that makes use of mathematical logic to produce new high-quality math problems and introduce new knowledge points that can give supervision for new mathematical reasoning logic. Expand
Learning by Fixing: Solving Math Word Problems with Weak Supervision
TLDR
To boost weaklysupervised learning, a novel learning-by-fixing (LBF) framework is proposed, which corrects the misperceptions of the neural network via symbolic reasoning and achieves comparable top-1 and much better top-3/5 answer accuracies than fully-supervised methods. Expand
Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints
We study the problem of generating arithmetic math word problems (MWPs) given a math equation that specifies the mathematical computation and a context that specifies the problem scenario. ExistingExpand
Investigating Math Word Problems using Pretrained Multilingual Language Models
TLDR
The experiments show that the MWP solvers may not be transferred to a different language even if the target expressions have the same operator set and constants, and it can be better generalized if problem types exist on both source language and target language. Expand
Solving Math Word Problems by Scoring Equations with Recursive Neural Networks
TLDR
This work explores novel approaches to score candidate solution equations using tree-structured recursive neural network (Tree-RNN) configurations using more established sequential representations, and improves overall performance and outperforms sequential LSTMs on such more complex problems. Expand
Injecting Numerical Reasoning Skills into Language Models
TLDR
This work shows that numerical reasoning is amenable to automatic data generation, and thus one can inject this skill into pre-trained LMs, by generating large amounts of data, and training in a multi-task setup. Expand
Solving arithmetic word problems by scoring equations with recursive neural networks
TLDR
This work explores novel approaches to score candidate solution equations using tree-structured recursive neural network (Tree-RNN) configurations using more established sequential representations, and improves overall performance and outperforms sequential LSTMs on such more complex problems. Expand
SMART: A Situation Model for Algebra Story Problems via Attributed Grammar
TLDR
This work introduces the concept of a situation model, which originates from psychology studies to represent the mental states of humans in problem-solving, and proposes SMART, which adopts attributed grammar as the representation of situation models for algebra story problems. Expand
Towards Question Format Independent Numerical Reasoning: A Set of Prerequisite Tasks
TLDR
This work introduces NUMBERGAME, a multifaceted benchmark to evaluate model performance across numerical reasoning tasks of eight diverse formats, and takes forward the recent progress in generic system development, demonstrating the scope of under-explored tasks. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 27 REFERENCES
Using Intermediate Representations to Solve Math Word Problems
TLDR
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
  • Subhro Roy, D. Roth
  • Computer Science
  • Transactions of the Association for Computational Linguistics
  • 2018
TLDR
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
TLDR
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
TLDR
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
TLDR
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
TLDR
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
TLDR
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
TLDR
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
TLDR
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
TLDR
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
...
1
2
3
...