@inproceedings{asprino-etal-2022-uncovering,
title = "Uncovering Values: Detecting Latent Moral Content from Natural Language with Explainable and Non-Trained Methods",
author = "Asprino, Luigi and
Bulla, Luana and
De Giorgis, Stefano and
Gangemi, Aldo and
Marinucci, Ludovica and
Mongiovi, Misael",
editor = "Agirre, Eneko and
Apidianaki, Marianna and
Vuli{\'c}, Ivan",
booktitle = "Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures",
month = may,
year = "2022",
address = "Dublin, Ireland and Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.deelio-1.4",
doi = "10.18653/v1/2022.deelio-1.4",
pages = "33--41",
abstract = "Moral values as commonsense norms shape our everyday individual and community behavior. The possibility to extract moral attitude rapidly from natural language is an appealing perspective that would enable a deeper understanding of social interaction dynamics and the individual cognitive and behavioral dimension. In this work we focus on detecting moral content from natural language and we test our methods on a corpus of tweets previously labeled as containing moral values or violations, according to Moral Foundation Theory. We develop and compare two different approaches: (i) a frame-based symbolic value detector based on knowledge graphs and (ii) a zero-shot machine learning model fine-tuned on a task of Natural Language Inference (NLI) and a task of emotion detection. The final outcome from our work consists in two approaches meant to perform without the need for prior training process on a moral value detection task.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="asprino-etal-2022-uncovering">
<titleInfo>
<title>Uncovering Values: Detecting Latent Moral Content from Natural Language with Explainable and Non-Trained Methods</title>
</titleInfo>
<name type="personal">
<namePart type="given">Luigi</namePart>
<namePart type="family">Asprino</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Luana</namePart>
<namePart type="family">Bulla</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Stefano</namePart>
<namePart type="family">De Giorgis</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aldo</namePart>
<namePart type="family">Gangemi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ludovica</namePart>
<namePart type="family">Marinucci</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Misael</namePart>
<namePart type="family">Mongiovi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures</title>
</titleInfo>
<name type="personal">
<namePart type="given">Eneko</namePart>
<namePart type="family">Agirre</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marianna</namePart>
<namePart type="family">Apidianaki</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ivan</namePart>
<namePart type="family">Vulić</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Dublin, Ireland and Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Moral values as commonsense norms shape our everyday individual and community behavior. The possibility to extract moral attitude rapidly from natural language is an appealing perspective that would enable a deeper understanding of social interaction dynamics and the individual cognitive and behavioral dimension. In this work we focus on detecting moral content from natural language and we test our methods on a corpus of tweets previously labeled as containing moral values or violations, according to Moral Foundation Theory. We develop and compare two different approaches: (i) a frame-based symbolic value detector based on knowledge graphs and (ii) a zero-shot machine learning model fine-tuned on a task of Natural Language Inference (NLI) and a task of emotion detection. The final outcome from our work consists in two approaches meant to perform without the need for prior training process on a moral value detection task.</abstract>
<identifier type="citekey">asprino-etal-2022-uncovering</identifier>
<identifier type="doi">10.18653/v1/2022.deelio-1.4</identifier>
<location>
<url>https://aclanthology.org/2022.deelio-1.4</url>
</location>
<part>
<date>2022-05</date>
<extent unit="page">
<start>33</start>
<end>41</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Uncovering Values: Detecting Latent Moral Content from Natural Language with Explainable and Non-Trained Methods
%A Asprino, Luigi
%A Bulla, Luana
%A De Giorgis, Stefano
%A Gangemi, Aldo
%A Marinucci, Ludovica
%A Mongiovi, Misael
%Y Agirre, Eneko
%Y Apidianaki, Marianna
%Y Vulić, Ivan
%S Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland and Online
%F asprino-etal-2022-uncovering
%X Moral values as commonsense norms shape our everyday individual and community behavior. The possibility to extract moral attitude rapidly from natural language is an appealing perspective that would enable a deeper understanding of social interaction dynamics and the individual cognitive and behavioral dimension. In this work we focus on detecting moral content from natural language and we test our methods on a corpus of tweets previously labeled as containing moral values or violations, according to Moral Foundation Theory. We develop and compare two different approaches: (i) a frame-based symbolic value detector based on knowledge graphs and (ii) a zero-shot machine learning model fine-tuned on a task of Natural Language Inference (NLI) and a task of emotion detection. The final outcome from our work consists in two approaches meant to perform without the need for prior training process on a moral value detection task.
%R 10.18653/v1/2022.deelio-1.4
%U https://aclanthology.org/2022.deelio-1.4
%U https://doi.org/10.18653/v1/2022.deelio-1.4
%P 33-41
Markdown (Informal)
[Uncovering Values: Detecting Latent Moral Content from Natural Language with Explainable and Non-Trained Methods](https://aclanthology.org/2022.deelio-1.4) (Asprino et al., DeeLIO 2022)
ACL