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Constrained Deep Answer Sentence Selection

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10372135" target="_blank" >RIV/00216208:11320/17:10372135 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1007/978-3-319-64206-2_7" target="_blank" >https://doi.org/10.1007/978-3-319-64206-2_7</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-319-64206-2_7" target="_blank" >10.1007/978-3-319-64206-2_7</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Constrained Deep Answer Sentence Selection

  • Original language description

    We propose Constrained Deep Neural Network (CDNN) a deep neural model for answer sentence selection in the context of Question Answering (QA) systems. To produce the best predictions, CDNN combines neural reasoning with a kind of symbolic constraint. It integrates pattern matching technique into sentence vector learning. When trained using enough samples, CDNN outperforms the other best models for sentence selection. We show how the use of other sources of training can enhance the performance of CDNN. In a well-studied dataset for answer sentence selection, our model improves the state-of-the-art significantly

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2017

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    20th International Conference, TSD 2017 Prague, Czech Republic, August 27–31, 2017 Proceedings

  • ISBN

    978-3-319-64205-5

  • ISSN

    0302-9743

  • e-ISSN

    neuvedeno

  • Number of pages

    9

  • Pages from-to

    57-65

  • Publisher name

    Springer International Publishing

  • Place of publication

    Cham / Heidelberg / New York

  • Event location

    Praha, Czechia

  • Event date

    Aug 27, 2016

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article