@inproceedings{sedefoglu-etal-2024-leadempathy-expert,
title = "{L}ead{E}mpathy: An Expert Annotated {G}erman Dataset of Empathy in Written Leadership Communication",
author = "Sedefoglu, Didem and
Lahnala, Allison Claire and
Wagner, Jasmin and
Flek, Lucie and
Ohly, Sandra",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.894",
pages = "10237--10248",
abstract = "Empathetic leadership communication plays a pivotal role in modern workplaces as it is associated with a wide range of positive individual and organizational outcomes. This paper introduces LeadEmpathy, an innovative expert-annotated German dataset for modeling empathy in written leadership communication. It features a novel theory-based coding scheme to model cognitive and affective empathy in asynchronous communication. The final dataset comprises 770 annotated emails from 385 participants who were allowed to rewrite their emails after receiving recommendations for increasing empathy in an online experiment. Two independent annotators achieved substantial inter-annotator agreement of {\textgreater}= .79 for all categories, indicating that the annotation scheme can be applied to produce high-quality, multidimensional empathy ratings in current and future applications. Beyond outlining the dataset{'}s development procedures, we present a case study on automatic empathy detection, establishing baseline models for predicting empathy scores in a range of ten possible scores that achieve a Pearson correlation of 0.816 and a mean squared error of 0.883. Our dataset is available at https://github.com/caisa-lab/LEAD-empathy-dataset.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sedefoglu-etal-2024-leadempathy-expert">
<titleInfo>
<title>LeadEmpathy: An Expert Annotated German Dataset of Empathy in Written Leadership Communication</title>
</titleInfo>
<name type="personal">
<namePart type="given">Didem</namePart>
<namePart type="family">Sedefoglu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Allison</namePart>
<namePart type="given">Claire</namePart>
<namePart type="family">Lahnala</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jasmin</namePart>
<namePart type="family">Wagner</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lucie</namePart>
<namePart type="family">Flek</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sandra</namePart>
<namePart type="family">Ohly</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Nicoletta</namePart>
<namePart type="family">Calzolari</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Min-Yen</namePart>
<namePart type="family">Kan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Veronique</namePart>
<namePart type="family">Hoste</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alessandro</namePart>
<namePart type="family">Lenci</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sakriani</namePart>
<namePart type="family">Sakti</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nianwen</namePart>
<namePart type="family">Xue</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ELRA and ICCL</publisher>
<place>
<placeTerm type="text">Torino, Italia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Empathetic leadership communication plays a pivotal role in modern workplaces as it is associated with a wide range of positive individual and organizational outcomes. This paper introduces LeadEmpathy, an innovative expert-annotated German dataset for modeling empathy in written leadership communication. It features a novel theory-based coding scheme to model cognitive and affective empathy in asynchronous communication. The final dataset comprises 770 annotated emails from 385 participants who were allowed to rewrite their emails after receiving recommendations for increasing empathy in an online experiment. Two independent annotators achieved substantial inter-annotator agreement of \textgreater= .79 for all categories, indicating that the annotation scheme can be applied to produce high-quality, multidimensional empathy ratings in current and future applications. Beyond outlining the dataset’s development procedures, we present a case study on automatic empathy detection, establishing baseline models for predicting empathy scores in a range of ten possible scores that achieve a Pearson correlation of 0.816 and a mean squared error of 0.883. Our dataset is available at https://github.com/caisa-lab/LEAD-empathy-dataset.</abstract>
<identifier type="citekey">sedefoglu-etal-2024-leadempathy-expert</identifier>
<location>
<url>https://aclanthology.org/2024.lrec-main.894</url>
</location>
<part>
<date>2024-05</date>
<extent unit="page">
<start>10237</start>
<end>10248</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T LeadEmpathy: An Expert Annotated German Dataset of Empathy in Written Leadership Communication
%A Sedefoglu, Didem
%A Lahnala, Allison Claire
%A Wagner, Jasmin
%A Flek, Lucie
%A Ohly, Sandra
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F sedefoglu-etal-2024-leadempathy-expert
%X Empathetic leadership communication plays a pivotal role in modern workplaces as it is associated with a wide range of positive individual and organizational outcomes. This paper introduces LeadEmpathy, an innovative expert-annotated German dataset for modeling empathy in written leadership communication. It features a novel theory-based coding scheme to model cognitive and affective empathy in asynchronous communication. The final dataset comprises 770 annotated emails from 385 participants who were allowed to rewrite their emails after receiving recommendations for increasing empathy in an online experiment. Two independent annotators achieved substantial inter-annotator agreement of \textgreater= .79 for all categories, indicating that the annotation scheme can be applied to produce high-quality, multidimensional empathy ratings in current and future applications. Beyond outlining the dataset’s development procedures, we present a case study on automatic empathy detection, establishing baseline models for predicting empathy scores in a range of ten possible scores that achieve a Pearson correlation of 0.816 and a mean squared error of 0.883. Our dataset is available at https://github.com/caisa-lab/LEAD-empathy-dataset.
%U https://aclanthology.org/2024.lrec-main.894
%P 10237-10248
Markdown (Informal)
[LeadEmpathy: An Expert Annotated German Dataset of Empathy in Written Leadership Communication](https://aclanthology.org/2024.lrec-main.894) (Sedefoglu et al., LREC-COLING 2024)
ACL