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Constrained Deep Answer Sentence Selection

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Text, Speech, and Dialogue (TSD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10415))

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Abstract

In this paper, we propose Constrained Deep Neural Network (CDNN) a simple deep neural model for answer sentence selection. CDNN makes its predictions based on neural reasoning compound with some symbolic constraints. It integrates pattern matching technique into sentence vector learning. When trained using enough samples, CDNN outperforms regular models. We show how using other sources of training data as a mean of transfer learning can enhance the performance of the network. In a well-studied dataset for answer sentence selection, our network improves the state of the art in answer sentence selection significantly.

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Notes

  1. 1.

    Compared to MRR and MAP, Accuracy is a pessimistic measure for this experiment, because it just considers the first selected answer and disregards the others.

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Acknowledgments

This research was partially funded by the Ministry of Education, Youth and Sports of the Czech Republic under SVV project number 260 453, core research funding, and GAUK 207-10/250098 of Charles University in Prague.

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Authors and Affiliations

  1. Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics, Charles University in Prague, Malostranske nam. 25, 11800, Praha 1, Czech Republic

    Ahmad Aghaebrahimian

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  1. Ahmad Aghaebrahimian
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Correspondence to Ahmad Aghaebrahimian .

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Editors and Affiliations

  1. University of West Bohemia, Pilsen, Czech Republic

    Kamil Ekštein

  2. University of West Bohemia, Pilsen, Czech Republic

    Václav Matoušek

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Aghaebrahimian, A. (2017). Constrained Deep Answer Sentence Selection. In: Ekštein, K., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2017. Lecture Notes in Computer Science(), vol 10415. Springer, Cham. https://doi.org/10.1007/978-3-319-64206-2_7

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  • DOI: https://doi.org/10.1007/978-3-319-64206-2_7

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  • Publisher Name: Springer, Cham

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