@inproceedings{sullivan-etal-2023-university,
title = "University at Buffalo at {S}em{E}val-2023 Task 11: {MASDA}{--}Modelling Annotator Sensibilities through {D}is{A}ggregation",
author = "Sullivan, Michael and
Yasin, Mohammed and
Jacobs, Cassandra L.",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.135",
doi = "10.18653/v1/2023.semeval-1.135",
pages = "978--985",
abstract = "Modeling the most likely label when an annotation task is perspective-dependent discards relevant sources of variation that come from the annotators themselves. We present three approaches to modeling the controversiality of a particular text. First, we explicitly represented annotators using annotator embeddings to predict the training signals of each annotator{'}s selections in addition to a majority class label. This method leads to reduction in error relative to models without these features, allowing the overall result to influence the weights of each annotator on the final prediction. In a second set of experiments, annotators were not modeled individually but instead annotator judgments were combined in a pairwise fashion that allowed us to implicitly combine annotators. Overall, we found that aggregating and explicitly comparing annotators{'} responses to a static document representation produced high-quality predictions in all datasets, though some systems struggle to account for large or variable numbers of annotators.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sullivan-etal-2023-university">
<titleInfo>
<title>University at Buffalo at SemEval-2023 Task 11: MASDA–Modelling Annotator Sensibilities through DisAggregation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Michael</namePart>
<namePart type="family">Sullivan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mohammed</namePart>
<namePart type="family">Yasin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Cassandra</namePart>
<namePart type="given">L</namePart>
<namePart type="family">Jacobs</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Atul</namePart>
<namePart type="given">Kr.</namePart>
<namePart type="family">Ojha</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">A</namePart>
<namePart type="given">Seza</namePart>
<namePart type="family">Doğruöz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Giovanni</namePart>
<namePart type="family">Da San Martino</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Harish</namePart>
<namePart type="family">Tayyar Madabushi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ritesh</namePart>
<namePart type="family">Kumar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elisa</namePart>
<namePart type="family">Sartori</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Toronto, Canada</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Modeling the most likely label when an annotation task is perspective-dependent discards relevant sources of variation that come from the annotators themselves. We present three approaches to modeling the controversiality of a particular text. First, we explicitly represented annotators using annotator embeddings to predict the training signals of each annotator’s selections in addition to a majority class label. This method leads to reduction in error relative to models without these features, allowing the overall result to influence the weights of each annotator on the final prediction. In a second set of experiments, annotators were not modeled individually but instead annotator judgments were combined in a pairwise fashion that allowed us to implicitly combine annotators. Overall, we found that aggregating and explicitly comparing annotators’ responses to a static document representation produced high-quality predictions in all datasets, though some systems struggle to account for large or variable numbers of annotators.</abstract>
<identifier type="citekey">sullivan-etal-2023-university</identifier>
<identifier type="doi">10.18653/v1/2023.semeval-1.135</identifier>
<location>
<url>https://aclanthology.org/2023.semeval-1.135</url>
</location>
<part>
<date>2023-07</date>
<extent unit="page">
<start>978</start>
<end>985</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T University at Buffalo at SemEval-2023 Task 11: MASDA–Modelling Annotator Sensibilities through DisAggregation
%A Sullivan, Michael
%A Yasin, Mohammed
%A Jacobs, Cassandra L.
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F sullivan-etal-2023-university
%X Modeling the most likely label when an annotation task is perspective-dependent discards relevant sources of variation that come from the annotators themselves. We present three approaches to modeling the controversiality of a particular text. First, we explicitly represented annotators using annotator embeddings to predict the training signals of each annotator’s selections in addition to a majority class label. This method leads to reduction in error relative to models without these features, allowing the overall result to influence the weights of each annotator on the final prediction. In a second set of experiments, annotators were not modeled individually but instead annotator judgments were combined in a pairwise fashion that allowed us to implicitly combine annotators. Overall, we found that aggregating and explicitly comparing annotators’ responses to a static document representation produced high-quality predictions in all datasets, though some systems struggle to account for large or variable numbers of annotators.
%R 10.18653/v1/2023.semeval-1.135
%U https://aclanthology.org/2023.semeval-1.135
%U https://doi.org/10.18653/v1/2023.semeval-1.135
%P 978-985
Markdown (Informal)
[University at Buffalo at SemEval-2023 Task 11: MASDA–Modelling Annotator Sensibilities through DisAggregation](https://aclanthology.org/2023.semeval-1.135) (Sullivan et al., SemEval 2023)
ACL