Detecting Early Signs of Depression in the Conversational Domain: The Role of Transfer Learning in Low-Resource Scenarios

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00359317" target="_blank" >RIV/68407700:21230/22:00359317 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21730/22:00359317

Result on the web

<a href="https://doi.org/10.1007/978-3-031-08473-7_33" target="_blank" >https://doi.org/10.1007/978-3-031-08473-7_33</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-08473-7_33" target="_blank" >10.1007/978-3-031-08473-7_33</a>

Result language

angličtina

Original language name

Detecting Early Signs of Depression in the Conversational Domain: The Role of Transfer Learning in Low-Resource Scenarios

Original language description

The high prevalence of depression in society has given rise to the need for new digital tools to assist in its early detection. To this end, existing research has mainly focused on detecting depression in the domain of social media, where there is a sufficient amount of data. However, with the rise of conversational agents like Siri or Alexa, the conversational domain is becoming more critical. Unfortunately, there is a lack of data in the conversational domain. We perform a study focusing on domain adaptation from social media to the conversational domain. Our approach mainly exploits the linguistic information preserved in the vector representation of text. We describe transfer learning techniques to classify users who suffer from early signs of depression with high recall. We achieve state-of-the-art results on a commonly used conversational dataset, and we highlight how the method can easily be used in conversational agents. We publicly release all source code.

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

12

Pages from-to

358-369

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

000870296500033