# Comparing Attention-based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading Comprehension

arXiv: https://arxiv.org/abs/1808.08744 · HTML: https://mlsanity.com/paper/1808.08744/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2018.

## Built on by (most cited first, 19 of 19)

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- [From text to multimodal: a survey of adversarial example generation in question answering systems](https://mlsanity.com/paper/2312.16156/) (2023)
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## Abstract (arXiv)

We propose a machine reading comprehension model based on the compare-aggregate framework with two-staged attention that achieves state-of-the-art results on the MovieQA question answering dataset. To investigate the limitations of our model as well as the behavioral difference between convolutional and recurrent neural networks, we generate adversarial examples to confuse the model and compare to human performance. Furthermore, we assess the generalizability of our model by analyzing its differences to human inference,