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Metric Learning and Adaptive Boundary for Out-of-Domain Detection

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00359318" target="_blank" >RIV/68407700:21230/22:00359318 - isvavai.cz</a>

  • Alternative codes found

    RIV/68407700:21730/22:00359318

  • Result on the web

    <a href="https://doi.org/10.1007/978-3-031-08473-7_12" target="_blank" >https://doi.org/10.1007/978-3-031-08473-7_12</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-031-08473-7_12" target="_blank" >10.1007/978-3-031-08473-7_12</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Metric Learning and Adaptive Boundary for Out-of-Domain Detection

  • Original language description

    Conversational agents are usually designed for closed-world environments. Unfortunately, users can behave unexpectedly. Based on the open-world environment, we often encounter the situation that the training and test data are sampled from different distributions. Then, data from different distributions are called out-of-domain (OOD). A robust conversational agent needs to react to these OOD utterances adequately. Thus, the importance of robust OOD detection is emphasized. Unfortunately, collecting OOD data is a challenging task. We have designed an OOD detection algorithm independent of OOD data that outperforms a wide range of current state-of-the-art algorithms on publicly available datasets. Our algorithm is based on a simple but efficient approach of combining metric learning with adaptive decision boundary. Furthermore, compared to other algorithms, we have found that our proposed algorithm has significantly improved OOD performance in a scenario with a lower number of classes while preserving the accuracy for in-domain (IND) classes.

  • 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

    2022

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Natural Language Processing and Information Systems

  • ISBN

    978-3-031-08472-0

  • ISSN

    0302-9743

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    127-134

  • Publisher name

    Springer, Cham

  • Place of publication

  • Event location

    Valencia

  • Event date

    Jun 15, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000870296500012