@inproceedings{terenziani-2025-temporal,
title = "Temporal Relation Classification: {An} {XAI} Perspective",
author = "Terenziani, Sofia Elena",
editor = "Johansson, Richard and
Stymne, Sara",
booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)",
month = mar,
year = "2025",
address = "Tallinn, Estonia",
publisher = "University of Tartu Library",
url = "https://aclanthology.org/2025.nodalida-1.72/",
pages = "714--728",
ISBN = "978-9908-53-109-0",
abstract = "Temporal annotations are used to identify and mark up temporal information, offering definition into how it is expressed through linguistic properties in text. This study investigates various discriminative pre-trained language models of differing sizes on a temporal relation classification task. We define valid reasoning strategies based on the linguistic principles that guide commonly used temporal annotations. Using a combination of saliency-based and counterfactual explanations, we examine if the models' decisions are in line with these strategies. Our findings suggest that the selected models do not rely on the expected linguistic cues for processing temporal information effectively."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="terenziani-2025-temporal">
<titleInfo>
<title>Temporal Relation Classification: An XAI Perspective</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sofia</namePart>
<namePart type="given">Elena</namePart>
<namePart type="family">Terenziani</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-03</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Richard</namePart>
<namePart type="family">Johansson</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sara</namePart>
<namePart type="family">Stymne</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>University of Tartu Library</publisher>
<place>
<placeTerm type="text">Tallinn, Estonia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">978-9908-53-109-0</identifier>
</relatedItem>
<abstract>Temporal annotations are used to identify and mark up temporal information, offering definition into how it is expressed through linguistic properties in text. This study investigates various discriminative pre-trained language models of differing sizes on a temporal relation classification task. We define valid reasoning strategies based on the linguistic principles that guide commonly used temporal annotations. Using a combination of saliency-based and counterfactual explanations, we examine if the models’ decisions are in line with these strategies. Our findings suggest that the selected models do not rely on the expected linguistic cues for processing temporal information effectively.</abstract>
<identifier type="citekey">terenziani-2025-temporal</identifier>
<location>
<url>https://aclanthology.org/2025.nodalida-1.72/</url>
</location>
<part>
<date>2025-03</date>
<extent unit="page">
<start>714</start>
<end>728</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Temporal Relation Classification: An XAI Perspective
%A Terenziani, Sofia Elena
%Y Johansson, Richard
%Y Stymne, Sara
%S Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)
%D 2025
%8 March
%I University of Tartu Library
%C Tallinn, Estonia
%@ 978-9908-53-109-0
%F terenziani-2025-temporal
%X Temporal annotations are used to identify and mark up temporal information, offering definition into how it is expressed through linguistic properties in text. This study investigates various discriminative pre-trained language models of differing sizes on a temporal relation classification task. We define valid reasoning strategies based on the linguistic principles that guide commonly used temporal annotations. Using a combination of saliency-based and counterfactual explanations, we examine if the models’ decisions are in line with these strategies. Our findings suggest that the selected models do not rely on the expected linguistic cues for processing temporal information effectively.
%U https://aclanthology.org/2025.nodalida-1.72/
%P 714-728
Markdown (Informal)
[Temporal Relation Classification: An XAI Perspective](https://aclanthology.org/2025.nodalida-1.72/) (Terenziani, NoDaLiDa 2025)
ACL
- Sofia Elena Terenziani. 2025. Temporal Relation Classification: An XAI Perspective. In Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025), pages 714–728, Tallinn, Estonia. University of Tartu Library.