LongEval: Longitudinal Evaluation of Model Performance at CLEF 2023

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AS248VU9A" target="_blank" >RIV/00216208:11320/23:S248VU9A - isvavai.cz</a>

Výsledek na webu

<a href="https://www.webofscience.com/wos/woscc/summary/06ff7b3b-7e51-4590-a737-664e7ff2151f-bb8928e9/relevance/1" target="_blank" >https://www.webofscience.com/wos/woscc/summary/06ff7b3b-7e51-4590-a737-664e7ff2151f-bb8928e9/relevance/1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-28241-6_58" target="_blank" >10.1007/978-3-031-28241-6_58</a>

Jazyk výsledku

angličtina

Název v původním jazyce

LongEval: Longitudinal Evaluation of Model Performance at CLEF 2023

Popis výsledku v původním jazyce

"In this paper, we describe the plans for the first LongEval CLEF 2023 shared task dedicated to evaluating the temporal persistence of Information Retrieval (IR) systems and Text Classifiers. The task is motivated by recent research showing that the performance of these models drops as the test data becomes more distant, with respect to time, from the training data. LongEval differs from traditional shared IR and classification tasks by giving special consideration to evaluating models aiming to mitigate performance drop over time. We envisage that this task will draw attention from the IR community and NLP researchers to the problem of temporal persistence of models, what enables or prevents it, potential solutions and their limitations."

Název v anglickém jazyce

LongEval: Longitudinal Evaluation of Model Performance at CLEF 2023

Popis výsledku anglicky

"In this paper, we describe the plans for the first LongEval CLEF 2023 shared task dedicated to evaluating the temporal persistence of Information Retrieval (IR) systems and Text Classifiers. The task is motivated by recent research showing that the performance of these models drops as the test data becomes more distant, with respect to time, from the training data. LongEval differs from traditional shared IR and classification tasks by giving special consideration to evaluating models aiming to mitigate performance drop over time. We envisage that this task will draw attention from the IR community and NLP researchers to the problem of temporal persistence of models, what enables or prevents it, potential solutions and their limitations."

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

"ADVANCES IN INFORMATION RETRIEVAL, ECIR 2023, PT III"

ISBN

978-3-031-28240-9

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

7

Strana od-do

499-505

Název nakladatele

Springer International Publishing Ag

Místo vydání

Cham

Místo konání akce

Cham

Datum konání akce

1. 1. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000995495200058