@inproceedings{lalor-etal-2024-item,
title = "Item Response Theory for Natural Language Processing",
author = "Lalor, John P. and
Rodriguez, Pedro and
Sedoc, Jo{\~a}o and
Hernandez-Orallo, Jose",
editor = "Mesgar, Mohsen and
Lo{\'a}iciga, Sharid",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-tutorials.2",
pages = "9--13",
abstract = "This tutorial will introduce the NLP community to Item Response Theory (IRT; Baker 2001). IRT is a method from the field of psychometrics for model and dataset assessment. IRT has been used for decades to build test sets for human subjects and estimate latent characteristics of dataset examples. Recently, there has been an uptick in work applying IRT to tasks in NLP. It is our goal to introduce the wider NLP community to IRT and show its benefits for a number of NLP tasks. From this tutorial, we hope to encourage wider adoption of IRT among NLP researchers.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lalor-etal-2024-item">
<titleInfo>
<title>Item Response Theory for Natural Language Processing</title>
</titleInfo>
<name type="personal">
<namePart type="given">John</namePart>
<namePart type="given">P</namePart>
<namePart type="family">Lalor</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Pedro</namePart>
<namePart type="family">Rodriguez</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">João</namePart>
<namePart type="family">Sedoc</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jose</namePart>
<namePart type="family">Hernandez-Orallo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-03</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts</title>
</titleInfo>
<name type="personal">
<namePart type="given">Mohsen</namePart>
<namePart type="family">Mesgar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sharid</namePart>
<namePart type="family">Loáiciga</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">St. Julian’s, Malta</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This tutorial will introduce the NLP community to Item Response Theory (IRT; Baker 2001). IRT is a method from the field of psychometrics for model and dataset assessment. IRT has been used for decades to build test sets for human subjects and estimate latent characteristics of dataset examples. Recently, there has been an uptick in work applying IRT to tasks in NLP. It is our goal to introduce the wider NLP community to IRT and show its benefits for a number of NLP tasks. From this tutorial, we hope to encourage wider adoption of IRT among NLP researchers.</abstract>
<identifier type="citekey">lalor-etal-2024-item</identifier>
<location>
<url>https://aclanthology.org/2024.eacl-tutorials.2</url>
</location>
<part>
<date>2024-03</date>
<extent unit="page">
<start>9</start>
<end>13</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Item Response Theory for Natural Language Processing
%A Lalor, John P.
%A Rodriguez, Pedro
%A Sedoc, João
%A Hernandez-Orallo, Jose
%Y Mesgar, Mohsen
%Y Loáiciga, Sharid
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F lalor-etal-2024-item
%X This tutorial will introduce the NLP community to Item Response Theory (IRT; Baker 2001). IRT is a method from the field of psychometrics for model and dataset assessment. IRT has been used for decades to build test sets for human subjects and estimate latent characteristics of dataset examples. Recently, there has been an uptick in work applying IRT to tasks in NLP. It is our goal to introduce the wider NLP community to IRT and show its benefits for a number of NLP tasks. From this tutorial, we hope to encourage wider adoption of IRT among NLP researchers.
%U https://aclanthology.org/2024.eacl-tutorials.2
%P 9-13
Markdown (Informal)
[Item Response Theory for Natural Language Processing](https://aclanthology.org/2024.eacl-tutorials.2) (Lalor et al., EACL 2024)
ACL
- John P. Lalor, Pedro Rodriguez, João Sedoc, and Jose Hernandez-Orallo. 2024. Item Response Theory for Natural Language Processing. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts, pages 9–13, St. Julian’s, Malta. Association for Computational Linguistics.