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

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>

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

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Czech description

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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)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

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

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