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        <title>Comparing Attention-Based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading Comprehension</title>
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        <namePart type="given">Matthias</namePart>
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    <name type="personal">
        <namePart type="given">Xiang</namePart>
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            <namePart type="given">Ivan</namePart>
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    <abstract>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, drawing upon insights from cognitive science.</abstract>
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%0 Conference Proceedings
%T Comparing Attention-Based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading Comprehension
%A Blohm, Matthias
%A Jagfeld, Glorianna
%A Sood, Ekta
%A Yu, Xiang
%A Vu, Ngoc Thang
%Y Korhonen, Anna
%Y Titov, Ivan
%S Proceedings of the 22nd Conference on Computational Natural Language Learning
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F blohm-etal-2018-comparing
%X 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, drawing upon insights from cognitive science.
%R 10.18653/v1/K18-1011
%U https://aclanthology.org/K18-1011
%U https://doi.org/10.18653/v1/K18-1011
%P 108-118

Markdown (Informal)

[Comparing Attention-Based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading Comprehension](https://aclanthology.org/K18-1011) (Blohm et al., CoNLL 2018)

• Comparing Attention-Based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading Comprehension (Blohm et al., CoNLL 2018)

ACL

• Matthias Blohm, Glorianna Jagfeld, Ekta Sood, Xiang Yu, and Ngoc Thang Vu. 2018. Comparing Attention-Based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading Comprehension. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pages 108–118, Brussels, Belgium. Association for Computational Linguistics.