Explaining Math Word Problem Solvers
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Abstract (arXiv)
Automated math word problem solvers based on neural networks have successfully managed to obtain 70-80% accuracy in solving arithmetic word problems. However, it has been shown that these solvers may rely on superficial patterns to obtain their equations. In order to determine what information math word problem solvers use to generate solutions, we remove parts of the input and measure the model's performance on the perturbed dataset. Our results show that the model is not sensitive to the removal of many words from the input and can still manage to find a correct answer when given a nonsense question. This indicates that automatic solvers do not follow the semantic logic of math word problems, and may be overfitting to the presence of specific words.
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References · 10
- 2021Are NLP Models really able to Solve Simple Math Word Problems?cited by 878
- 2017Adversarial Examples for Evaluating Reading Comprehension Systemscited by 571
- 2021A Diverse Corpus for Evaluating and Developing English Math Word Problem Solverscited by 318
- 2016Understanding Neural Networks through Representation Erasurecited by 224
- 2018Pathologies of Neural Models Make Interpretations Difficultcited by 140
- 2019AllenNLP Interpret: A Framework for Explaining Predictions of NLP Modelscited by 74
- 2021Adversarial Examples for Evaluating Math Word Problem Solverscited by 20
- 2022Practice Makes a Solver Perfect: Data Augmentation for Math Word Problem Solverscited by 9
- 2019Solving Arithmetic Word Problems Automatically Using Transformer and Unambiguous Representationscited by 4
- 2018QADiver: Interactive Framework for Diagnosing QA Modelscited by 3
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