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        <title>Detecting cognitive impairments by agreeing on interpretations of linguistic features</title>
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        <namePart type="given">Zining</namePart>
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    <abstract>Linguistic features have shown promising applications for detecting various cognitive impairments. To improve detection accuracies, increasing the amount of data or the number of linguistic features have been two applicable approaches. However, acquiring additional clinical data can be expensive, and hand-crafting features is burdensome. In this paper, we take a third approach, proposing Consensus Networks (CNs), a framework to classify after reaching agreements between modalities. We divide linguistic features into non-overlapping subsets according to their modalities, and let neural networks learn low-dimensional representations that agree with each other. These representations are passed into a classifier network. All neural networks are optimized iteratively. In this paper, we also present two methods that improve the performance of CNs. We then present ablation studies to illustrate the effectiveness of modality division. To understand further what happens in CNs, we visualize the representations during training. Overall, using all of the 413 linguistic features, our models significantly outperform traditional classifiers, which are used by the state-of-the-art papers.</abstract>
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%0 Conference Proceedings
%T Detecting cognitive impairments by agreeing on interpretations of linguistic features
%A Zhu, Zining
%A Novikova, Jekaterina
%A Rudzicz, Frank
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F zhu-etal-2019-detecting
%X Linguistic features have shown promising applications for detecting various cognitive impairments. To improve detection accuracies, increasing the amount of data or the number of linguistic features have been two applicable approaches. However, acquiring additional clinical data can be expensive, and hand-crafting features is burdensome. In this paper, we take a third approach, proposing Consensus Networks (CNs), a framework to classify after reaching agreements between modalities. We divide linguistic features into non-overlapping subsets according to their modalities, and let neural networks learn low-dimensional representations that agree with each other. These representations are passed into a classifier network. All neural networks are optimized iteratively. In this paper, we also present two methods that improve the performance of CNs. We then present ablation studies to illustrate the effectiveness of modality division. To understand further what happens in CNs, we visualize the representations during training. Overall, using all of the 413 linguistic features, our models significantly outperform traditional classifiers, which are used by the state-of-the-art papers.
%R 10.18653/v1/N19-1146
%U https://aclanthology.org/N19-1146
%U https://doi.org/10.18653/v1/N19-1146
%P 1431-1441

Markdown (Informal)

[Detecting cognitive impairments by agreeing on interpretations of linguistic features](https://aclanthology.org/N19-1146) (Zhu et al., NAACL 2019)

• Detecting cognitive impairments by agreeing on interpretations of linguistic features (Zhu et al., NAACL 2019)

ACL

• Zining Zhu, Jekaterina Novikova, and Frank Rudzicz. 2019. Detecting cognitive impairments by agreeing on interpretations of linguistic features. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1431–1441, Minneapolis, Minnesota. Association for Computational Linguistics.