• Corpus ID: 209202200

Neural Module Networks for Reasoning over Text

@article{Gupta2020NeuralMN,
  title={Neural Module Networks for Reasoning over Text},
  author={Nitish Gupta and Kevin Lin and Dan Roth and Sameer Singh and Matt Gardner},
  journal={ArXiv},
  year={2020},
  volume={abs/1912.04971}
}
Answering compositional questions that require multiple steps of reasoning against text is challenging, especially when they involve discrete, symbolic operations. Neural module networks (NMNs) learn to parse such questions as executable programs composed of learnable modules, performing well on synthetic visual QA domains. However, we find that it is challenging to learn these models for non-synthetic questions on open-domain text, where a model needs to deal with the diversity of natural… 

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