@inproceedings{valer-etal-2026-annotators,
title = "Annotators of a Feather {FLOCKE} Together: A Framework to Analyze Latent Group Patterns Based on Annotator Behavior",
author = "Valer, Giovanni and
Leonardelli, Elisa and
Casula, Camilla and
Tonelli, Sara and
Lombardi, Michele",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2665/",
pages = "60662--60676",
ISBN = "979-8-89176-459-0",
abstract = "Accounting for disagreement among annotators is important for a variety of NLP applications. However, understanding what insights can be drawn from disaggregated annotations remains an open question. We introduce FLOCKE, a framework that jointly learns a task and models latent groups of annotators with similar behavior. We first formalize six analyses enabled by our framework, including annotator-group discovery, group- and individual-level label prediction, and finding the items most prone to cause disagreement. We then validate FLOCKE with a controlled synthetic benchmark, creating predefined annotator groups and labeling patterns, and showing that it can recover group structure with very high accuracy. We apply FLOCKE to two real-world datasets, where it predicts individual annotations, discovers interpretable annotator groups, and shows that groups are not explained by socio-demographic traits alone."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="valer-etal-2026-annotators">
<titleInfo>
<title>Annotators of a Feather FLOCKE Together: A Framework to Analyze Latent Group Patterns Based on Annotator Behavior</title>
</titleInfo>
<name type="personal">
<namePart type="given">Giovanni</namePart>
<namePart type="family">Valer</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elisa</namePart>
<namePart type="family">Leonardelli</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Camilla</namePart>
<namePart type="family">Casula</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sara</namePart>
<namePart type="family">Tonelli</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Michele</namePart>
<namePart type="family">Lombardi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hung-Yi</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sunipa</namePart>
<namePart type="family">Dev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jessy</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Desmond</namePart>
<namePart type="family">Elliott</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-459-0</identifier>
</relatedItem>
<abstract>Accounting for disagreement among annotators is important for a variety of NLP applications. However, understanding what insights can be drawn from disaggregated annotations remains an open question. We introduce FLOCKE, a framework that jointly learns a task and models latent groups of annotators with similar behavior. We first formalize six analyses enabled by our framework, including annotator-group discovery, group- and individual-level label prediction, and finding the items most prone to cause disagreement. We then validate FLOCKE with a controlled synthetic benchmark, creating predefined annotator groups and labeling patterns, and showing that it can recover group structure with very high accuracy. We apply FLOCKE to two real-world datasets, where it predicts individual annotations, discovers interpretable annotator groups, and shows that groups are not explained by socio-demographic traits alone.</abstract>
<identifier type="citekey">valer-etal-2026-annotators</identifier>
<location>
<url>https://aclanthology.org/2026.emnlp-main.2665/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>60662</start>
<end>60676</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Annotators of a Feather FLOCKE Together: A Framework to Analyze Latent Group Patterns Based on Annotator Behavior
%A Valer, Giovanni
%A Leonardelli, Elisa
%A Casula, Camilla
%A Tonelli, Sara
%A Lombardi, Michele
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F valer-etal-2026-annotators
%X Accounting for disagreement among annotators is important for a variety of NLP applications. However, understanding what insights can be drawn from disaggregated annotations remains an open question. We introduce FLOCKE, a framework that jointly learns a task and models latent groups of annotators with similar behavior. We first formalize six analyses enabled by our framework, including annotator-group discovery, group- and individual-level label prediction, and finding the items most prone to cause disagreement. We then validate FLOCKE with a controlled synthetic benchmark, creating predefined annotator groups and labeling patterns, and showing that it can recover group structure with very high accuracy. We apply FLOCKE to two real-world datasets, where it predicts individual annotations, discovers interpretable annotator groups, and shows that groups are not explained by socio-demographic traits alone.
%U https://aclanthology.org/2026.emnlp-main.2665/
%P 60662-60676
Markdown (Informal)
[Annotators of a Feather FLOCKE Together: A Framework to Analyze Latent Group Patterns Based on Annotator Behavior](https://aclanthology.org/2026.emnlp-main.2665/) (Valer et al., EMNLP 2026)
ACL